Search

Energy crises are becoming a problem for many, most of the energy being produced is utilized in buildings. Building design and materials that are used in buildings play a vital role in saving electricity. Sustainable cities are one of the eleventh goal of Sustainable Development Goals to be achieved and this study is in context of SDGs for developing tools to create this globe sustainable and efficient society for humans. Phase change material is a type of material that can absorb and release heat energy as it changes between solid and liquid states. This ability to store and release thermal energy makes PCM a promising material for use in building insulation, Heating Ventilation Air Conditioning Systems, and other energy efficient applications. The use of PCM in buildings has the potential to significantly reduce energy consumption and improve thermal comfort levels. By incorporating PCM into building envelopes, such as walls, roofs, and floors, the material can absorb excess heat during the day and release it at night, helping to regulate indoor temperatures and reduce the need for heating and cooling systems. This study investigates the impact of using PCM in building envelopes. In this regard office building rooms are modelled in CAD software and are simulated in energy plus software. The simulation results are validated by considering the experiment and numerical study as a reference from paper. Model room is simulated for Hyderabad, Pakistan. Building envelopes without PCM have conduction rate of around 106.6 W/m2, whereas building envelopes with PCM have conduction rate around 42 W/m2. This shows that utilization of PCM reduced heat conduction up to around 40%. PCM reduced energy consumption of around 33.25 kWh/year/m2. Results show that without PCM air temperature was above the thermal comfort value. it reached 300C in hot months, it reduced up to 270C with the incorporation of PCM.

The purpose of this paper is to introduce the class of large MR-Kannan contractions on Banach space that contains the classes of Kannan, enriched Kannan, large Kannan, MR-Kannan contractions and some other classes of nonlinear operators. Some examples are presented to support the concepts introduced herein. We prove the existence of a unique fixed point for such a class of operators in Banach spaces.

This research implements an evolutionary optimized finite differences scheme (FDS) for nonlinear electrohydrodynamics ion drag flow dynamics in a cylindrical conduit (EHD-IDFCC). In the scheme, finite differences are exploited to discretize governing expressions of EHD-IDFCC, represented with a nonlinear singular seconder order differential equation, into a nonlinear equations-based system. The fitness function based on residual error is constructed for the FDS-based discretized EHD-IDFCC model. Its optimization is conducted with the global search capability of genetic algorithms (GAs) aided by the local search efficacy of the interior-point method (IPM), i.e., FDS-GA-IPM. The performance of the designed stochastic numerical solver FDS-GA-IPM is evaluated for a variant of the EHD-IDFCC model for different scenarios to measure the effect of axial flow velocity by varying the electric Hartmann numbers, as well as the nonlinearity factor. Statistical interpretations based on histogram illustrations, probability plots, and boxplots in terms of the cost function, mean absolute error (MAE), Thai inequality coefficients (TIC), and coefficient of determination metrics (R2) are used to endorse the accuracy, convergence, stability, and strength of the FDS-GA-IPM.

The dynamics of plant-herbivore interactions are essential for understanding ecosystem stability and resilience. This article investigated the effects of incorporating a harvesting effect on the dynamics of a discrete-time plant-herbivore system. An analysis was performed to determine the existence and stability of fixed points. In addition, studies have shown that the system experienced transcritical, period-doubling, and Neimark-Sacker bifurcations. Moreover, we provided numerical simulations to substantiate our theoretical results. Our research indicated that harvesting in excessive amounts may have negative effects on the populations of both plants and herbivores. However, when harvesting was done at moderate levels, it promoted the coexistence and stability of both populations. The findings of our analysis provided a deep understanding of the intricate dynamics of ecological systems and underscored the need to use sustainable harvesting methods for the management and preservation of ecosystems.

Sesame crop is widely recognized as contributor in global oil production. Potassium (K) and zinc (Zn) being responsible for oil synthesis pathways were applied in a field trial at agronomy research farm of Agriculture University Peshawar to assess their effects on sesame during summer season 2022. Four Zn foliar sprays (0, 2, 4 and 6 %) and four K levels (0, 60, 80 and 100 kg ha-1 ) were tested in a randomized complete design with three replications. Results exhibited that K and Zn interaction for all parameters was non-significant. However, K and Zn at 60 kg ha-1 and 4 % Zn ha-1 solely applied enhanced agronomic and quality attributes such as plant height (174.0 and 176.2 cm), branches/ plant (12.3 and 12.0), capsules/ plant (70 and 71), seeds/capsule (55 and 55), thousand seeds weight (3.0 and 3.0 g), flowering days (53 and 52), maturity days (104 and 102) biological yield (3583 and 3585 kg ha-1 ), seed yield (872 and 861 kg ha-1 ), oil content (47.4 and 48.3 %), oil yield (417 and 416 kg ha- 1 )respectively. Moreover, 100 kg K recorded higher potassium content (3.85 %) while Zn spray at 4 % noted higher Zn content (1.63 %) and seed protein content (20.1 %). Correlation analysis showed positive impact of increasing seed yield on all quality attributes. Thus, applying K at 60 kg and 4 % Zn foliar ha-1 is recommended for improved yield and quality of sesame in agro-climatic conditions of Peshawar region.

BackgroundThe escalating number of deaths related to opioid usage has intensified the pursuit of non-opioid alternatives for managing chronic pain. It's often observed that psychiatric comorbidities coexist in patients suffering from chronic pain. There are a variety of psychotropic medications that have demonstrated effectiveness in treating both psychiatric symptoms and pain. This systematic review and meta-analysis aim to assess the effectiveness of various psychiatric drugs in managing specific types of chronic pain, including fibromyalgia, neuropathic pain, and chronic low back pain.MethodsA comprehensive search of five major databases was conducted through February 2023 to identify randomized controlled trials (RCTs) that met our inclusion criteria, focusing on outpatients Over 18 years of age with chronic pain. The study assessed the effectiveness of duloxetine, mirogabalin, pregabalin, gabapentin, and tricyclic antidepressants (TCAs), including serotonin-norepinephrine reuptake inhibitors (SNRIs), across various chronic pain conditions such as fibromyalgia, neuropathic pain, and chronic low back pain. The primary outcome measures included pain reduction, improvement in function, and quality of life. Of the 29 RCTs in the systematic review, 20 studies qualified for the meta-analysis. The analysis was stratified by pain type and treatment duration (short-term ≤14 weeks vs. long-term >14 weeks), using Hedge's g standardized mean differences and a random-effects model, along with sensitivity and subgroup analyses.ResultsThe overall short-term intervention effect across all studies was significant (SMD −1.45, 95% CI −2.15 to −0.75, p

Throughout the history of power systems, power cables have been used to securely and efficiently distribute electrical energy to the destined locations. Cross-linked polyethylene (XLPE), a commonly used insulator in high-voltage cables, have several desirable properties, such as low dielectric loss, high dielectric constant, high thermal conductivity, enhanced thermal stability, and superior resistance against electrical stress. However, further improvements of XLPE's performance are needed. The incorporation of large specific surface area nanoparticles, such as boron nitride nanosheets and graphene oxide, exhibited a great potential in enhancing XLPE's properties. These nanoparticles create numerous trapping sites, even at small loading levels, due to their large interfacial regions. In addition, voltage stabilisers with polar groups can scavenge high-energy electrons generated by local electric fields, thereby inhibiting the electrical tree growth. Another important aspect of enhancing XLPE's dielectric performance is the inclusion of antioxidants with phenolic groups. These antioxidants react with peroxyl radicals, mitigating their harmful effects. This review summarises the effects of nanoparticles, voltage stabilisers, antioxidants, and polymer amalgamation on dielectric performance of XLPE as an insulation material. The major challenges in dielectric insulation such as breakdown voltage strength, electrical tree growth, structural defect, space charge accumulation, and thermal aging are addressed.

Abstract Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

The fabric quality in textile industry is characterized by the texture (weave pattern) as it plays a vital role for the production and design of best quality fabric. The earlier proposed automated weave identification methods based on image processing techniques are highly dependent on the lighting conditions. The machine learning methods have been reported to show better accuracy. However, they require very large training datasets, very high processing power and computation time. This study proposes improved accuracy with smaller dataset and reduced computation time by proposing a modification of VGG16 model by adding two additional pooling layers. Using evaluation metrics of both models, the modified model results were analysed according to accuracy, balanced accuracy, and F1-score. On the basis of investigational outcomes, a comparison has been performed with earlier work. The results show that the proposed VGG-16 model is capable to achieve state-of-the-art accuracy and avoid unnecessary activation features by freezing the main convolutional base layers. Ultimately, as evidenced by the performance of the modified VGG-16 deep learning model, the proposed method demonstrated improved accuracy. The study results show that the proposed modified VGG16 algorithm is able to recognize the features of provided database with 90% accuracy and F1-Score ranging from 0.8 to1.

Image processing has widespread uses practically in every branch of science and arts. Processing images is more difficult than processing sound or data as there are more bits in the high pixel quality image. It requires more space to store the image, more bandwidth to transmit it, and more time and resources to process. An image's complexity may decrease if its bit size is decreased. Sigma-delta modulation, or SDM for short, is an alternative method of minimizing data-word length to compression. Digital signal processing (DSP) systems can be made simpler by using the SDM approach, which was first created for analog to digital conversion (ADC). This paper suggests a novel way to use SDM in MATLAB for improved image processing. Consequently, the suggested single-bit SDM-based image arithmetic architecture is tested and compared with the traditional image arithmetic techniques. Additionally, to see the noisy channel influence on the traditional and proposed systems, some statistical metrics are also studied at different noise variance values, such as signal to noise ratio (SNR), mean square error (MSE), and Peak SNR value. The suggested architecture for the SDM-based image arithmetic precisely matches the addition and subtraction results of the conventional design, even yielding a higher SNR and the same Peak SNR as the traditional methods. In contrast, the outcomes of division and multiplication fall within an acceptable range. For better results the over-sampling ratio (OSR), an inherent characteristic of SDM must be increased at the cost of more processing cycles. Therefore, the trade-off between fewer resources, limited transmission bandwidth, and comparatively more cycles is provided by the SDM-based technique.

Background High fat content of pancreas can contribute to its soft texture, which is a strong predictor for postoperative pancreatic fistula (POPF). We propose to assess the relationship of pancreatic computed tomography (CT) attenuation index (PAI) with histopathological fat content of pancreas and postpancreatectomy outcomes.

Abstract Cancer is responsible for approximately 10 million deaths worldwide, with 70% of the deaths occurring in low- and middle-income countries; as such safer and more effective anti-cancer drugs are required. Therefore, the potential benefits of Ziziphus nummularia and Ziziphus spina-christi as sources of anti-cancer agents were investigated. Z. nummularia and Z. spina-christi extracts were prepared using chloroform, ethanol, ethyl acetate, and water. The extracts’ anti-cancer properties were determined using the MTT Cell Viability Assay in four cancer cell lines: breast (KAIMRC2 and MDA-MB-231), colorectal (HCT8), and liver (HepG2). The ApoTox-Glo Triplex Assay and high-content imaging (HCI)-Apoptosis Assay were used to assess KAIMRC2 and HCT8 cells further. In addition, KAIMRC2 cells were tested for microtubule staining, and AKT/mTOR protein expression was determined by western blot analysis. Liquid chromatography-mass spectrometry (LC–MS) was performed to identify the secondary metabolites in the ethanol and ethyl acetate extracts, followed by in silico techniques to predict molecular targets and interactions, safety, and pharmacokinetic profile for identified metabolites. Out of the eight extracts, the ethanolic extract of Z. nummularia, exhibited the most potent activity against KAIMRC2 cells with an IC50 value of 29.2 μg/ml. Cancer cell treatment with the ethanolic extract of Z. nummularia resulted in a dose-dependent decrease in cell viability with increased apoptosis and cytotoxic effects. Microtubule staining showed a disrupted microtubular network. The ethanolic extract treatment of KAIMRC2 cells led to upregulated expression of pAKT and pmTOR. In silico studies predicted luteolin-7-O-glucoside to be a ligand for tubulin with the highest docking score (− 7.686) and similar binding interactions relative to the native ligand. Further computational analysis of the metabolites showed acceptable pharmacokinetic and safety profiles, although ethanolic extract metabolites were predicted to have cardiotoxic effects. Ethanolic extraction is optimal for solubilizing active anticancer metabolites from Z. nummularia, which may act by causing M-phase arrest via inhibition of tubulin polymerization. Luteolin-7-O-glucoside is the lead candidate for further research and development as an anti-cancer agent. In addition, this study suggests that herbal treatment could switch on mechanisms of adaptation and survival in cancer cells.

The refuge effect is critical in ecosystems for stabilizing predator-prey interactions. The purpose of this research was to investigate the complexities of a discrete-time predator-prey system with a refuge effect. The analysis investigated the presence and stability of fixed points, as well as period-doubling and Neimark-Sacker (NS) bifurcations. The bifurcating and fluctuating behavior of the system was controlled via feedback and hybrid control methods. In addition, numerical simulations were performed as evidence to back up our theoretical findings. According to our findings, maintaining an optimal level of refuge availability was critical for predator and prey population cohabitation and stability.

This study aims to investigate the effects of climate change on return level of extreme maximum temperature (EMT) events in Iran. To this end, the CRU gridded dataset was used to collect EMT for the 1901–2014 period and future data were projected from four available CMIP6 models, where the BCC-CSM2-MR performed best under the latest Shared Socioeconomic Pathways-Representative concentration pathways (SSPs-RCPs) emission scenarios for the 2015–2100 period. The non-stationary state of the distribution was considered under three models GEV0 (location and scale parameters are constant), GEV1 (nonstationary of location), and GEV2 (nonstationary of scale) based on the evaluation criteria . The findings indicate that, when using a non-stationary approach and considering the SSP5-8.5 scenario for a 2-year return period, the return level of extreme temperature increased by up to +4°C compared with the stationary approach, while considering a non-stationary approach without climate change, the increase in the return level of extreme temperature was much smaller(up to +0.7°C). MCMC and DE-MC showed no significant differences and demonstrated that all stations are non-stationary in terms of the location parameter (GEV1). HIGHLIGHTS Temperature frequency was analyzed based on the Bayesian method (MCMC and DEMC) under three generalized extreme value models.; The impact of the non-stationary approach was investigated on the return level of maximum temperatures during historical and future periods.; Intra-period trends were assessed in the extreme temperature due to climate change (CMIP6).;

Abstracts: Edible oils play a vital role in human nutrition, serving as a crucial source of energy, essential fatty acids, and vitamins necessary for bodily functions. However, during handling, cooking, and storage these oils are susceptible to oxidation, resulting in the loss of nutritional value, the development of undesirable flavors, the production of harmful compounds, and a reduction in shelf life. To counteract oxidation and enhance oil stability, synthetic antioxidants have traditionally been employed. However, in recent times, consumer awareness has led to an increased preference for natural antioxidants because of the harmful effects of synthetic antioxidants on human health. This review provides a comprehensive overview of recent research focused on the oxidative stability of various edible vegetable oils through the incorporation of natural antioxidants derived from fruits and vegetable waste. These antioxidants obtained from waste through diverse techniques are blended with different vegetable oils in the form of free extracts, encapsulated extracts, nano-encapsulated extracts, and nano-emulsion oil extracts to assess their antioxidant efficacy. These antioxidant-rich waste extracts efficiently prevented oxidation by scavenging free radicals, chelating metal ions, and delaying the formation of peroxides, offering a safer and more effective alternative to synthetic counterparts. Beyond their antioxidative properties, these extracts contribute to the nutritional enhancement of oils, functioning as nutraceuticals. This approach not only offers a natural means to protect edible oils but also facilitates efficient waste valorization, mitigating potential environmental impacts and making it a sustainable and eco-friendly strategy.

AbstractFinancial risk has received increasing attention from policymakers and financial institutions. Therefore, the present study examines the relationship between capital and risk for Jordanian banks by using data from 2010–2019. The study employs fixed effect, random effect, GMM, and 3SLS. Our findings show that the capital requirement regulation has a positive impact on capital and risk rates. Moreover, the study also concludes that Jordanian banks hold more than the minimum regulatory capital requirements laid down by Basel II, III, and the CBJ. The banking sector increases its capital adequacy by raising its liquidity and reducing its tendency to take risks. Our results indicate a highly significant negative relationship between Jordanian commercial bank capital and risk. Liquidity risk, ROA and stock market capitalization are positively related to bank capital. The results of the study suggest that Jordanian banks should be involved in higher-risk lending actions and help increase competition in the banking sector.

ABSTRACTAsia is the largest producer of shrimp, accounting for 70% of the global cultured production. The market for shrimp has a high demand and the by-products from shrimp processing make up 40–60% of the whole shrimp. The main by-products include the head, viscera, shell, pelopods, tail, cephalothorax, and exoskeleton. The trends of the future and hurdles of shrimp by-product utilization have been outlined. These by-products are a good source of protein and have anti-inflammatory, anti-fungal, and anti-oxidant properties. They also enhance the immune system and have bioactive compounds that encourage their use for anti-cancerous, anti-hypertensive, and various other diseases. The waste produced can yield valuable by-products, including astaxanthin, oil, carotenoids, fortified products, nutrient-enriched chitin, protein, flavor enhancers, and composite flour. The yield of astaxanthin is 59.97 µg/g along with carotenoid is 68.26 µg/ml dw, chitosan (87%), protein (47.8%), oil extracted (88.9%) from shrimp by-products, and shrimp head is a rich source of protein (66%) and chitin (6%). These by-products can help meet the growing demand of an increasing population. The abundance of healthy ingredients found in shrimp makes it a valuable resource for scientists, entrepreneurs, and industrialists to develop new products. Additionally, utilizing shrimp waste can help reduce the burden on the earth and decrease environmental pollution.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Post-acute myocardial infarction (AMI) fibrosis is a pathophysiologic process characterised by activation of the profibrotic mediator, transforming growth factor-β (TGF-β). AMI is associated with a substantial increase in the levels of extracellular adenosine triphosphate (eATP), which acts on the purinergic P2X7-receptor (P2X7-R) and triggers an inflammatory response that contributes to myocardial fibrotic remodelling. P2X7-R has been implicated in several cardiovascular diseases; however, its role in the regulation of cardiac fibrosis remains unclear. Therefore, the current study aimed to determine the effect of the P2X7-R antagonist, A740003, on post-AMI fibrosis, via the profibrotic TGF-β1/Smad signalling pathway, and elucidate whether its effect is mediated via the modulation of GSK-3β. AMI was induced by surgical ligation of the left anterior descending coronary artery, Thereafter, animals were divided into groups: sham control, MI-untreated, MI-vehicle, and MI-A740003 (50 mg/kg/day) and treated for seven days accordingly. The heart weight/body weight ratio of untreated-ligated rats significantly increased by 15.1 %, creatine kinase‐MB (CK‐MB) significantly increased by 40 %, troponin‐I levels significantly increased by 25.4 %, and lactate dehydrogenase significantly increased by 47.2 %, indicating myocardial damage confirmed by morphological changes and massive cardiac fibrosis. The protein expression of cardiac fibronectin, TGF-β1, and p-Smad2 were also upregulated by 143 %, 40 %, and 8 %, respectively, indicating cardiac fibrosis. The treatment of ligated rats with A740003 led to improvement in all the above-mentioned parameters. Overall, A740003 exhibits potential cardio-protective effects on post-AMI fibrotic remodelling in the animal model of AMI through P2X7-R blockade, possibly by downregulating the profibrotic TGF-β1/Smad signalling pathway and restoring GSK-3β phosphorylation. Altogether, treatment with A740003 could serve as a new cardioprotective strategy to attenuate post-AMI fibrotic remodelling.

The production of new pavements involving recycled asphalt pavement (RAP) is beneficial for preserving petroleum asphalt and natural aggregates. However, increasing the RAP content raises performance concerns in new pavement, thus necessitating the use of rejuvenators. This research examined the potential of using petroleum sludge (PS) as a rejuvenator for RAP recycling. Gas chromatography-mass spectrometry (GCMS) detected the PS hydrocarbons involved in rejuvenation. The dynamic shear rheometer (DSR) provided the rheological data of rejuvenated binders. The rheological data were subsequently processed using response surface methodology (RSM) to develop rutting and fatigue models. RSM was also employed to model the Marshall and volumetric properties of the asphalt mixture. Atomic force microscopy (AFM) of PS rejuvenated RAP binder revealed a new microstructure containing protrusions that resembled the morphology of lightweight bitumen fractions. These rejuvenating effects were attributed to lightweight hydrocarbons of PS, which supplement the maltenes fractions in aged asphalt. The multi-objective optimization provided the RAP and bitumen content of 90% and 4.79% to meet the required PWD specifications.

Objective: To determine the diagnostic accuracy of Doppler assessment of hepatic venous waveforms for predicting large esophageal varices in patients with cirrhosis, keeping esophagogastroduodenoscopy as a gold standard. Study Design: Cross-sectional study. Place and Duration of Study: Armed Forces Institute of Radiology & Imaging, Rawalpindi Pakistan, from Jan 2021 to Jan 2022. Methodology: According to the Child-Pugh classification, 157 cases of liver cirrhosis were included in this study. With a convex probe operating at 3.5 to 5 MHz, a Doppler ultrasound was executed. If quiet breathing wasn't an option, the spectral waveform was captured at the end of the inhalation phase while holding your breath. This was roughly 3–6 cm from where it connects to the inferior vena cava. All patients underwent an esophagogastroduodenoscopy. Results: In Doppler USG-positive patients, 84 (true positive) developed significant esophageal varices on endoscopy, while 4 (false positive) did not. Among 69 individuals with negative Doppler USG results, six (false negative) had significant esophageal varices on endoscopy, whereas 63 (true negative) did not (p=0.0001). We found that the overall sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of Doppler evaluation of hepatic venous waveforms were 93.33%, 94.0%, 95.45%, 91.30%, and 93.6% for finding large esophageal varices in cirrhotic patients using esophagogastroduodenoscopy as the gold standard. Conclusion: This study proved that using Doppler to look at hepatic vein waveforms is a very sensitive and accurate noninvasive way to tell if someone with cirrhosis will get big esophageal varices.

Objective: To compare the efficacy of 5% Permethrin with the oral Ivermectin for management of scabies. Study Design: Quasi-experimental Study. Place and Duration of Study: Department of Dermatology, CMH, Abbottabad Pakistan, from Jun to Nov 2021. Methodology: Our study enrolled 100 patients, aged 5-80 years, after informed consent. Demographic information and study variables were noted in a data collection tool. All participants were randomly assigned to one of two groups i.e. Group A received Permethrin 5% twice with a one-week interval, whereas Group B received a single dose of oral Ivermectin. The evaluation was conducted at 2-4 week intervals. Results: From our sample of 100 participants, 57 were females and 43 were males; age ranged from 5 to 80 years. Group A of Permethrin-5% 43(86%) patients’ symptoms improved than in Group B 24(48%) patients of oral Ivermectin. There was also a statistically significant relationship between Permethrin and Ivermectin (p-value

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

The dataset presented here was created by combining surveys conducted by Open Sourcing Mental Illness, a non-profit organization, from 2017 to 2021. The primary objective of the surveys was to assess the prevalence of mental health concerns among individuals employed in the technology sector and to gauge their attitudes toward mental health in the workplace.The dataset is filtered to include only those respondents with a primary tech role, and descriptive questions are removed, ensuring data consistency and validity of survey responses for effective analysis.The proposed dataset provides a valuable resource for researchers and practitioners to gain insights into the mental health concerns and attitudes of individuals employed in the technology sector, thus aiding the development of evidence-based interventions and policies to improve the well-being of employees.

Background: The morphology of megakaryocytes plays a major role in thrombopoiesis. Dysmegakaryocytopoiesis and thrombocytopenia can result from imperfection in any phase of megakaryocytopoiesis. The structure, morphology, and characteristics of megakaryocytes can differentiate and identify the etiology of underlying diseases such as leukaemia, mixed anaemia, idiopathic, aplasia, or infiltration. This research aimed to comprehend megakaryocyte attributions and their contribution to the diagnosis of thrombocytopenia. Methods: This was a cross-sectional analysis of all successive cases of thrombocytopenia bone marrow aspirates over 12 months (January 2019 to December 2019) at the Department of Pathology, Indus Medical College Hospital Tando Muhammad Khan. With a 100X lens, megakaryocyte morphology was studied. Data were analysed by SPSS and Chi-square was performed to find correlation at the 95% confidence interval. Results: Among the 91 subjects of thrombocytopenia, the commonest etiology of thrombocytopenia was idiopathic thrombocytopenia 34(37.36%). The common morphological changes in megakaryocytes were hypolobated megakaryocytes and bare megakaryocyte nuclei. Megakaryocyte numbers were increased for 31 (91.17%) patients among idiopathic thrombocytopenia, though the megakaryocyte numbers were decreased among 19 (90.47%) patients having acute leukaemia. A change to immature, young, less polyploid and bare nuclei forms was observed in immune thrombocytopenia. Immature types were seen in 15 cases, 14 cases of bare megakaryocytic nuclei, 15 cases of hypolobation and one case of micromegakaryocytes. Conclusion: Many similarities among various haematological diseases were observed in megakaryocytic morphology. However, in megakaryocytic thrombocytopenia, high megakaryocyte counts and evidence of bare nuclei of megakaryocytes (p=0.02) and hypolobic types (p=0.03) proved important and play a very essential role in diagnosis. Keywords: Megakaryocytes; Thrombocytopenia; Bone Marrow Aspirate; Diagnosis; Dysmegakaryopoiesis.

This research endeavor delves into the utilization of non-orthogonal multiple access (NOMA) techniques within the context of optical wireless sensor networks (OWSNs). The study entails an exploration of the viability of integrating NOMA into OWSNs and introduces a novel architectural framework that amalgamates successive interference cancellation (SIC) and superposition coding methodologies. This amalgamation aims to enhance the overall performance and spectral efficiency of the network. The investigation meticulously assesses the performance of the proposed architecture, employing diverse metrics such as bit error rate (BER), outage probability, and environmental impact. Empirical results underscore the commendable precision of the NOMA-based architecture when juxtaposed with conventional orthogonal multiple access (OMA) approaches. This distinction is particularly pronounced in scenarios involving a substantial number of sensor nodes. Furthermore, the research undertakes a comprehensive scrutiny of varying system parameters, such as user densities and power allocation factors, to gauge their influence on network accuracy. The discerned outcomes elucidate NOMA’s capacity to augment the operational efficacy of optical wireless sensor connections. The proposed architectural paradigm thus emerges as a promising avenue for optimizing resource allocation and utilization within OWSNs, imparting newfound potential to a spectrum of applications spanning industrial automation, environmental monitoring, and smart city implementations.

Wireless Body Area Networks (WBANs) have significantly transformed human life, particularly in sectors such as healthcare, fitness, entertainment, and sports. The strategic placement of sink nodes in WBANs plays a pivotal role in influencing network connectivity, power efficiency, and overall network performance. In the context of designing WBAN sink placement, the primary challenges revolve around ensuring energy efficiency and robust connectivity. For monitoring a patient’s vital signs, sensor nodes are implanted at various locations within the patient’s body, which transmit physiological data to a central hub known as a sink node. The selection of the optimal position for the sink node is crucial in minimizing node energy consumption during data transmission. Addressing the complexity of the problem, this paper introduces four approaches; (i) Weighted geometric-based sequential least square programming (WG-SLSQP). WG-SLSQP incorporates three key approaches: a) Geometric median b) Weighted average technique and c)Sequential least square programming technique, (ii) Humpback Whale Optimization Algorithm (HWOA), (iii) Distance-based random mean shift (D-RMS), and (iv) Voronoi-based Positioning (VP) for sink node placement. WG-SLSQP demonstrates greater stability and lower localization errors as compared to D-RMS, HWOA and VP. The residual energy of WG-SLSQP is 93.6%, D-RMS is measured at 92%, while VP and HWOA exhibit values of 90.4% and 90% respectively. Moreover, the Average Localization Error (ALE) for WG-SLSQP is 0.500 m, D-RMS is 0.568 m, whereas VP and HWOA have ALE values of 0.60 m and 0.619 m, respectively. The results indicate that the suggested WG-SLSQP approach outperforms its predecessors.

In the development of image forensics, detection of Copy-Move Forgery (CMF) has become a major challenge due to the proliferation of image forgery techniques. The CMF is widely utilized to alter the content of the original image to spread false information or to use such forged digital images for illegal purposes e.g. false evidence in the court of law, or to blackmailing any individual. This paper presents a new method for CMF Detection (CMFD) that uses the power of Graph Convolution Networks (GCNs) and its multiple layers with ReLU activation, for CMFD and analysis. The aim to use GCN is due to its ability to improve the feature extraction process by utilizing the spatial and structural affiliation between elements in the digital images. Also, the GCN aims to store information about images and use it to graphically describe images with pixels or image areas as features, spatial and correlation relationships as edges. By pulling data from this image, GCN is able to obtain content rich features that are very powerful at detecting CMF regions. In proposed methodology, we utilized Support Vector machine (SVM) for classification and the binary cross-entropy loss, and the Adam optimizer for improving accuracy. Our scheme successfully achieves high accuracy and is effective in CMFD. We use the MICC F220, and CoMoFoD datasets to test the GCN in our proposed CMFD method. Through much testing and evaluation, we have found that GCN has the tremendous ability for CMFD in digital images in term of accuracy.

Wireless Body Area Networks (WBANs) have significantly enhanced various aspects of human life, particularly in healthcare, fitness, entertainment, sports, and etc. In WBANs, the sensor nodes are placed in and around the body along with the sink node, which collects the physiological data from these sensors and forwards it for further processing. The placement of the sink node is one of the critical aspects in the design of WABNs as it affects both the energy efficiency and connectivity. To this end, this paper introduces a hybrid method called Distance and Angulation based AGglomerative Clustering (DAAG). DAAG, initially clusters the WBAN sensors using Distance and Angulation based k-Mean clustering. Afterward, Agglomerative Clustering is applied to determine the optimal placement of the sink node. The results of DAAG are compared with various machine learning and optimization approaches, including D-RMS (Distance based Random mean shift clustering), Reinforcement Q-Learning Approach (QL), Humpback Whale optimization (HWOA), Multi-Angulation (MA) and Closeness Centrality (CC). Given an initial energy, the results show that the DAAG exhibits superior performance in terms of latency, packet error rate (PER), and energy consumption. DAAG shows an energy consumption of only 1.51% outperforming QL, HWOA, MA, CC, and D-RMS along with an improved localization accuracy of 0.36 m.

The most frequent and dangerous hazard to the electrical insulation of high voltage (HV) and high field (HF) equipment is partial discharges (PD). The development of PD activity is both a cause and an indicator of insulation degradation, which could ultimately lead to insulation breakdown. For electrical power equipment to operate safely and dependably, continuous and efficient PD monitoring must be conveniently performed to prevent associated damages and any harm to electrical power equipment. This work presents the design of a new UHF antenna for detecting PD in medium-voltage gas-insulated applications. The working frequency band of the proposed UHF antenna is 370 MHz–1300 MHz, which translates into a total bandwidth of 930 MHz UHF frequency band. Over the whole operating frequency of the antenna, the return loss is less than -10dB. Recently, an eco-friendly insulating gas, HFO1336mzz(E), is considered to be a good substitute for sulfur hexafluoride (SF6) in medium-voltage gas-insulated equipment (MV-GIE). The HFO(E) gas in a mixture with CO2 can reach the dielectric strength of SF6 gas. Hence, the HFO(E)/CO2 gas mixture’s partial discharge properties are systematically investigated in this research work. In particular, a detailed examination focuses on the partial discharge extinction voltage (PDEV) and partial discharge inception voltage (PDIV) under varying electric field conditions at different gas pressure levels and mixing ratios. The PDIV of the HFO(E)/CO2 gas mixture exhibited a linear-saturation rising pattern with both mixing ratio and gas pressure, mirroring the trend observed in SF6 when the HFO(E) gas concentration ranged between 25% and 30%.

Gas-insulated systems are widely utilized in the electric power sector to transmit and distribute electrical energy. Sulphur-hexafluoride (SF6) has dominated gas insulation in high-voltage insulation technology since the early 60s. It is a greenhouse gas with a protracted lifespan in the atmosphere. This paper proposes an economical and comparatively more environmentally friendly R507 gas alternative to SF6 for medium-voltage applications. R507 has been analyzed experimentally through power frequency breakdown and lightning impulse testing to validate the performance and theoretical concepts. R507 has a very low liquefication temperature of $-46.7^{\circ }\text{C}$ , but it must still be mixed with buffer gases such as CO2, N2, or dry air to meet the diverse liquefaction temperature applications. Various field utilization factors under AC and lightning impulse voltages are used in the experiments, along with different electrode geometries, including sphere-to-plane and rod-to-plane (i.e., quasi-homogeneous and inhomogeneous electric field distribution). For comparison, identical experiments are conducted with pure SF6. R507 gas was found to be a promising substitute for SF6 gas, with its dielectric strength being approximately 0.95 times that of SF6 gas. A positive synergistic effect is present between R507 and CO2, along with the good self-recoverability property of the gas mixture. The current research study serves as a fundamental resource for characterizing the R507/CO2 gas mixture insulation properties to be utilized in practical applications.

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.

The Internet of Things (IoT) is an essential part of 5G, Beyond-5G (B5G), and 6G systems; it has several applications in two of the principal 5G use cases, namely ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), and in their successors within B5G and 6G: extreme ultra-reliable low-latency communication (eURLLC) and ultra-massive machine-type communication (umMTC). IoT systems, which are characterized by narrow bandwidths, have stringent requirements owing to the specific nature of their applications and use cases. The purpose of this study is to investigate and jointly optimize the energy efficiency (EE) and latency through resource allocation for IoT cellular systems. With regard to the contributions, in this study we investigated the optimization of EE in narrowband IoT systems, compared resource unit configurations (RUCs), jointly formulated the optimization of EE and latency, and introduced a suboptimal but efficient algorithm. More precisely, as the EE performance of various resource unit configurations has not been exhaustively investigated in the current state of the art, we analyzed and compared the EE of RUCs. The results show vast differences in performance between RUCs. For example, in terms of EE, the best RUC has an EE more than 80 times higher than the worst, which illustrates the importance of this investigation. We then proposed a scheduler based on the shortest job first (SJF) for minimum latency allocation, and another scheduler based on a joint evaluation of EE and latency. With respect to conventional techniques, these schedulers achieve a better trade-off between latency reduction and gain in terms of EE for a wider range of parameter configurations in multi-cellular layouts. The study demonstrates that in the presence of repetitions, algorithms that achieve high EE will mostly achieve low latency.

The objective of this study was to analyze the complex dynamics of a discrete-time predator-prey system by using the piecewise constant argument technique. The existence and stability of fixed points were examined. It was shown that the system experienced period-doubling (PD) and Neimark-Sacker (NS) bifurcations at the positive fixed point by using the center manifold and bifurcation theory. The management of the system's bifurcating and fluctuating behavior may be controlled via the use of feedback and hybrid control approaches. Both methods were effective in controlling bifurcation and chaos. Furthermore, we used numerical simulations to empirically validate our theoretical findings. The chaotic behaviors of the system were recognized through bifurcation diagrams and maximum Lyapunov exponent graphs. The stability of the positive fixed point within the optimal prey growth rate range $ A_1 < a < A_2 $ was highlighted by our observations. When the value of $ a $ falls below a certain threshold $ A_1 $, it becomes challenging to effectively sustain prey populations in the face of predation, thereby affecting the survival of predators. When the growth rate surpasses a specific threshold denoted as $ A_2 $, it initiates a phase of rapid expansion. Predators initially benefit from this phase because it supplies them with sufficient food. Subsequently, resource depletion could occur, potentially resulting in long-term consequences for populations of both the predator and prey. Therefore, a moderate amount of prey's growth rate was beneficial for both predator and prey populations.

The present work investigates the role of deep rolling surface treatment on the water droplet erosion behavior of 17–4 PH stainless steel. Standard heat treatments were first applied to samples of 17–4 PH stainless steel to achieve three states, solution treated and two different aging conditions. The mechanical properties of these conditions were determined using hardness and tensile tests. Then, deep rolling surface treatments were applied to the various conditions of the 17–4 PH. Water droplet erosion tests were subsequently performed at impact velocities of 250 m/s and 300 m/s, and the erosion performance in terms of incubation period and maximum erosion rate before and after the deep rolling treatment is compared. It was found that deep rolled samples exhibited considerably longer incubation periods compared to only heat-treated samples. The improvement in erosion resistance of the 17–4 PH stainless steel is attributed to its lower strain hardening exponent values, which reduces embrittlement that is usually associated with the work hardening due to deep rolling. Hence, it is concluded that low strain hardening exponent is essential to the effectiveness and success of mechanical surface treatments to combat water droplet erosion.

Abstract Parkinson’s Disease (PD) is the second most common neurodegenerative disorder. The pathological hallmark of PD is loss of dopaminergic neurons and the presence of aggregated α-synuclein, primarily in the substantia nigra pars compacta (SNpc) of the midbrain. However, the molecular mechanisms that underlie the pathology in different cell types is not currently understood. Here, we present a single nucleus transcriptome analysis of human post-mortem SNpc obtained from 15 sporadic Parkinson’s Disease (PD) cases and 14 Controls. Our dataset comprises ∼84K nuclei, representing all major cell types of the brain, allowing us to obtain a transcriptome-level characterization of these cell types. Importantly, we identify multiple subpopulations for each cell type and describe specific gene sets that provide insights into the differing roles of these subpopulations. Our findings reveal a significant decrease in neuronal cells in PD samples, accompanied by an increase in glial cells and T cells. Subpopulation analyses demonstrate a significant depletion of tyrosine hydroxylase (TH) enriched astrocyte, microglia and oligodendrocyte populations in PD samples, as well as TH enriched neurons, which are also depleted. Moreover, marker gene analysis of the depleted subpopulations identified 28 overlapping genes, including those associated with dopamine metabolism (e.g., ALDH1A1, SLC6A3 & SLC18A2). Overall, our study provides a valuable resource for understanding the molecular mechanisms involved in dopaminergic neuron degeneration and glial responses in PD, highlighting the existence of novel subpopulations and cell type-specific gene sets.

The economic relationship between Pakistan and China, which is founded on historical, geopolitical, and economic ties, has been substantially affected by the COVID-19 pandemic. This study analyzes the evolution of trade patterns between the two nations during the pandemic, casting light on how supply networks, trade volumes, and product compositions were impacted. From 2018 to 2022, exports and imports fluctuate, and Pakistan has a trade deficit with China, according to the data. The economic impact of COVID-19 on both nations is examined, as are government responses and relief measures. Despite the pandemic's effects on global trade and supply chains, trade volumes between Pakistan and China increased in 2021 due to the global economic recovery, rising Chinese demand for Pakistani products, and trade agreements. The research highlights the resiliency and adaptability of both nations during difficult periods. The findings highlight the significance of diversifying exports and imports, as well as the potential for future economic growth resulting from ongoing projects under the China-Pakistan Economic Corridor (CPEC) and efforts to balance their trade relationship. This research contributes to a comprehensive comprehension of the impact of the COVID-19 pandemic on Pakistan-China trade and the possibility of future economic cooperation.

Abstract Research studies have been carried out to accentuate Fennel Seed Spent, a by-product of the Nutraceutical Industry, as an inexpensive, recyclable and operational biosorbent for bioremediation of Acid Blue 113 (AB113) in simulated water-dye samples and textile industrial effluent (TIE). The physical process of adhesion of AB113 on the surface of the biosorbent depends on various parameters, such as the initial amount of the dye, amount and expanse of the biosorbent particles, pH of the solution and temperature of the medium. The data obtained was analyzed using three two-parameter and five three-parameter adsorption isotherm models to glean the adsorbent affinities and interaction mechanism of the adsorbate molecules and adsorbent surface. The adsorption feature study is conducted employing models of Weber-Morris, pseudo 1st and 2nd order, diffusion film model, Dumwald-Wagner and Avrami model. The study through 2nd order pseudo and Avrami models produced complementary results for the authentication of experimental data. The thermodynamic features, ΔG 0 , ΔH 0, and ΔS 0 of the adsorption process are acclaimed to be almost spontaneous, physical in nature and endothermic in their manifestation. Surface characterization was carried out using Scanner Electron Microscopy, and identification and determination of chemical species and molecular structure was performed using Infrared Spectroscopy (IR). Maximum adsorption evaluated using statistical optimization with different combinations of five independent variables to study the individual as well as combined effects by Fractional Factorial Experimental Design (FFED) was 236.18 mg g−1 under optimized conditions; pH of 2, adsorbent dosage of 0.500 g L−1, and an initial dye concentration of 209.47 mg L−1 for an adsorption time of 126.62 min with orbital shaking of 165 rpm at temperature 49.95 °C.

ABSTRACTTechniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time.

Abstract Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient’s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19 + CNN, ResNet152V2, Gated Recurrent Unit (GRU) + ResNet152V2, and ResNet152V2 + Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19 + CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19 + CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19 + CNN model has performed better in terms of improved accuracy.

Hareer Fatima,1 Hussain Sohail Rangwala,1 Muhammad Saqlain Mustafa,1 Muhammad Ashir Shafique,1 Syed Raza Abbas,2 Azra Rizwan,3 Tagwa Kalool Fadlalla Ahmed,4 Ainan Arshad3 1Department of Medicine, Jinnah Sindh Medical University, Karachi, Pakistan; 2Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan; 3Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan; 4Department of Medicine, Ahfad University for Women, Omdurman, Al-Khartum, SudanCorrespondence: Tagwa Kalool Fadlalla Ahmed, Department of Medicine, Ahfad University for Women, PO Box 167, Omdurman, Al-Khartum, Sudan, Tel +923412127759, Email tagwa.kalool@gmail.comIntroduction: Diabetes Mellitus (DM) is a significant global health concern, with Type 2 DM (T2DM) being highly prevalent. Glucagon-Like Peptide 1 receptor agonists (GLP-1RA), such as Danuglipron, offer potential benefits in T2DM management. This meta-analysis examines the safety and efficacy of Danuglipron, focusing on adverse outcomes and glycemic parameters.Methods: A systematic search was conducted in PubMed, Scopus, and Cochrane Library for RCTs involving Danuglipron till August 2023, following PRISMA guidelines. The Cochrane risk-of-bias tool was used for quality assessment. Adverse outcomes (diarrhea, nausea, vomiting, headache, decreased appetite, dyspepsia, dizziness) and glycemic parameters like changes in HbA1C, fasting plasma glucose (FPG), and body weight were analyzed.Results: Four RCTs published from 2021 to 2023 were included. Both doses of Danuglipron were associated with diarrhea (RR=2.66, 90% CI: 1.32 to 5.35, p=0.02), nausea (RR=5.5, 90% CI: 3.4 to 8.88, p< 0.00001), and vomiting (RR=5.98, 90% CI: 2.93 to 12.23, p=0.0001). The 120mg dose showed decreased appetite (RR=3.46, 90% CI: 1.57 to 7.62, p=0.01), dyspepsia (RR=4.04, 90% CI: 1.93 to 8.43, p=0.002), and dizziness (RR=5.08, 90% CI: 1.45 to 17.82, p=0.03). Reductions in HbA1C (SMD − 1.09, 90% CI − 1.39 to − 0.8, p < 0.00001), FPG (SMD − 1.10, 90% CI − 1.46 to − 0.75, p < 0.00001), and body weight (SMD − 1.08, 90% CI − 1.42 to − 0.74, p < 0.00001) were observed for both doses.Conclusion: Danuglipron demonstrates potential for glycemic control and weight reduction in T2DM. Adverse outcomes include diarrhea, nausea, vomiting, and decreased appetite, with dose-related effects. Clinicians must weigh benefits against side effects when considering Danuglipron for T2DM management.Keywords: diabetes mellitus, glucagon-like peptide 1 receptor agonists, Danuglipron, adverse outcomes, glycemic parameters

The production of amylose is facilitated by granule-bound starch synthase (GBSS). Despite its importance, the specific protein interactions involving barley grain-bound starch synthase Ia (HvGBSSIa) remain poorly understood. To elucidate this, we engineered a pET-32a-HvGBSSIa prokaryotic expression vector for specific expression in E. coli Rosetta cells. A rabbit anti-HvGBSSIa polyclonal antibody was generated and employed to enrich HvGBSSIa-binding proteins from barley grains through immunoprecipitation. The isolated complexes were then resolved through SDS-PAGE, and the constituent proteins were identified using mass spectrometry coupled with database searches. Our results confirmed the successful preparation of a highly specific polyclonal antibody against HvGBSSI. Furthermore, differential expression of HvGBSSIa was assessed across various barley tissues and developmental stages of the grain, revealing peak expression at 25 days post-flowering. Proteins interacting with HvGBSSIa, including sucrose synthase and starch branching enzyme, were identified through co-immunoprecipitation. This study lays the groundwork for further detailed analyses of the HvGBSSIa protein complex in barley.

The global dilemma of informal settlements persists alongside the fast process of urbanization. Various methods for analyzing remotely sensed images to identify informal settlements using semantic segmentation have been extensively researched, resulting in the development of numerous supervised and unsupervised algorithms. Texture-based analysis is a topic extensively studied in the literature. However, it is important to note that approaches that do not utilize a multiresolution strategy are unable to take advantage of the fact that texture exists at different spatial scales. The capacity to do online mapping and precise segmentation on a vast scale while considering the diverse characteristics present in remotely sensed images carries significant consequences. This research presents a novel approach for identifying informal settlements using multiresolution analysis and self-attention techniques. The technique shows potential for being resilient in the presence of inherent variability in remotely sensed images due to its capacity to extract characteristics at many scales and prioritize areas that contain significant information. Segmented pictures underwent an accuracy assessment, where a comparison analysis was conducted based on metrics such as mean intersection over union, precision, recall, F-score, and overall accuracy. The proposed method’s robustness is demonstrated by comparing it to various state-of-the-art techniques. This comparison is conducted using remotely sensed images that have different spatial resolutions and informal settlement characteristics. The proposed method achieves a higher accuracy of approximately 95%, even when dealing with significantly different image characteristics.

The Trichilia emetica plant is traditionally used for medicinal and food purposes. However, there are limited studies on the bioactivity and cytotoxicity of its seed butter and aril oil. This study aimed to assess the antidiabetic activity and cytotoxicity of seed butter and aril oil, obtained via two different extraction methods, and compare their lipid profiles. The plant samples were collected from the Faifa mountains and extracted using a Soxhlet apparatus for hot extraction and a magnetic stirrer for cold maceration. The antidiabetic activity and cytotoxicity were evaluated using the α-amylase and MTT assays, respectively. The fatty acids were quantified utilizing gas chromatography-mass spectrometry. This study proves the impact of the extraction method on the yield, cytotoxicity, antidiabetic activity and lipid profile. The highest cytotoxicity was observed with the seed butter obtained via Soxhlet extraction. The α-amylase inhibition was observed at the highest levels with the seed butter and aril oil obtained via cold maceration. The palmitic acid (PA) and oleic acid (OA) were detected at their maximal concentrations in the seed butter obtained via Soxhlet extraction and aril oil obtained via cold maceration, respectively. This study represents an essential basis for understanding the importance of T. emetica as a valuable tree for food, cosmetic and medicinal purposes. Further experiments can lead to the development of green extraction techniques and isolation of the cytotoxic and antidiabetic molecules that can be developed into new pharmaceutical products or serve as lead molecules for new drugs.

Objective: To compare the efficacy of 70% Glycolic acid peeling with Tranexamic acid mesotherapy in treating melasma. Study Design: Quasi-experimental study Place and Duration of Study: Department of Dermatology, CMH-Abbottabad Pakistan, Jun to Nov 2021. Methodology: The 54 patients aged 20-50 years were enrolled and randomly assigned into two groups. In Group-A, 27 patients prescribed 70% Glycolic acid every two weeks for 12 weeks (6 sessions) and in Group-B, 27 patients injected every week for 12 weeks (12 sessions) with Intradermal Tranexamic acid (4mg/ml). All patients had a clinical examination using the mMASI scale. On follow-up, evaluation was carried out on the 1st, sixth, and 12th weeks. Results: The majority of 35(65%) patients had mild MASI scores. The paired t-test results of Group A and Group B were both significant, while the post-treatment mean of Group A and B was insignificant. Conclusion: The study concludes that Glycolic acid and TXA are equally efficient in treating melasma. Moreover, Tranexamic acid reduced the recurrence of melasma, but topical application of Glycolic acid showed more compliance.

Tuberculosis (TB) remains a leading cause of death worldwide and is estimated to have caused 1.3 million deaths worldwide in 2022. Approximately one quarter of the world's population are infected with Mycobacterium tuberculosis, of whom up to 10% will progress to developing active TB disease. Achieving the World Health Organization End TB Strategy targets of a 95% reduction in TB mortality and a 90% reduction in TB incidence worldwide by 2035 remains a daunting task. The continuing spread of multidrug-resistant TB adds another obstacle to achieving global TB control. Larger funding pledges coupled with technological advances have recently enabled the enhancement of TB vaccine development efforts. These are yielding a pipeline of over 17 products currently in different stages of clinical trials. Emerging promising phase I and II trial results and advancement to phase III trials have necessitated “vaccine preparedness” in parallel so that a smooth transition from any positive clinical trial result to phase IV evaluation and implementation into policy and practice can follow. Promotion of a human rights–based approach, which recognizes and upholds the fundamental rights of all affected by the disease, is essential to ensure universal access to quality TB vaccines, regardless of their background or personal circumstances.

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

Complementary and alternative medicine (CAM) has attracted much interest, and its prevalence in both developed and developing countries has increased. During the Hajj season, millions of Muslims from many different countries travel to Makkah for the pilgrimage. In dealing with health issues during the holy season, many pilgrims prefer to self-medicate with traditional remedies instead of visiting medical practitioners, which could affect the efforts of state healthcare organizations to maintain overall public health during this mass gathering. This study aims to gauge the prevalence of CAM use during Hajj, and to assess pilgrims’ beliefs and knowledge of CAM therapies, with particular reference to products available in Makkah. A cross-sectional survey was conducted in several camps and hotels occupied by Hajj pilgrims in Makkah, during Hajj 2023. CAM modalities were used by 68.8 % of the study participants during the Hajj season. There were almost equal numbers of men (53.7 %) and women (46.3 %) participants, with 88 % of the CAM users being non-Saudi and only 12 % Saudi. The majority of the CAM users belonged to two age groups, the 31–40 year group (29.9 %) and the 41–50 year group (34.5 %). The most frequent self-practice therapies were religious prayer/rituals (30.2 %), and the most popular practitioner therapies was herbal treatments (12.3 %). The most common source of CAM-related information was family/friends (29.2 %), for improving well-being reason (25.8 %). More than half of the participants (56.8 %) strongly believed that CAM therapies have the potential to cure disease, although they were unaware of possible interactions between CAM and conventional drugs (76.7 %). More than half of the participants (57.8 %) did not disclose their CAM usage to healthcare practitioners. Half of the sample said they used CAMs during Hajj because of the common belief that therapeutic products from the holy city of Makkah, such as Zamzam water, are more effective. In conclusion, CAM therapies are commonly used by Hajj pilgrims as they are presumed to be natural and therefore safe, raising concerns about the potential risks of relying on CAM without adequate consultation with healthcare providers or awareness of potential interactions between prescription drugs and CAM treatments.