Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change.

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.

Geo-statistical analysis of climatic variability and trend detection in the Hindu Kush region, North Pakistan.

This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.

Isolation, derivatization, in-vitro, and in-silico studies of potent butyrylcholinesterase inhibitors from Berberis parkeriana Schneid.

Seven known isoquinoline alkaloids 1-7 were isolated from the root extracts of Berberis parkeriana Schneid. Nine new derivatives 8-16 of one of the isolated compounds, jatrorrhizine (7), were synthesized. All the isolated as well as derivatized compounds were evaluated for their in-vitro acetylcholinesterase (AChE), and butyrylcholinesterase (BChE) inhibitory activity. Functionalized compounds selectively exhibited a potent-to-moderate activity with IC50 = 5.5 ± 0.3-124.5 ± 0.4 µM against butyrylcholinesterase enzyme. Among them, compound 15 was a potent BChE inhibitor (IC50 = 5.5 ± 0.3 µM), as compared to the standard drug galantamine hydrobromide (IC50 = 40.83 ± 0.37 µM). Active compounds were further subjected to kinetic, and molecular docking studies to predict their modes of inhibition, and interactions with the receptor (BChE), respectively. Enzyme kinetics studies showed that compounds 9 (IC50 = 25.3 ± 0.5 µM), and 14 (IC50 = 23.9 ± 0.5 µM) were non-competitive inhibitors, while compound 15 exhibited a competitive inhibition. In addition, these compounds were found to be non-cytotoxic against human fibroblast (BJ) cell line, except 9 (IC50 = 17.1 ± 1.0 µM), and 10 (IC50 = 18.4 ± 0.3 µM). Inhibition of cholinesterases is an important approach for development of drugs against Alzheimer's disease, and thus discoveries presented here deserve further investigation.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Rainfall Prediction System Using Machine Learning Fusion for Smart Cities.

Precipitation in any form-such as rain, snow, and hail-can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Measurement Uncertainty, Purity, and Entanglement Dynamics of Maximally Entangled Two Qubits Interacting Spatially with Isolated Cavities: Intrinsic Decoherence Effect.

In a system of two charge-qubits that are initially prepared in a maximally entangled Bell's state, the dynamics of quantum memory-assisted entropic uncertainty, purity, and negative entanglement are investigated. Isolated external cavity fields are considered in two different configurations: coherent-even coherent and even coherent cavity fields. For different initial cavity configurations, the temporal evolution of the final state of qubits and cavities is solved analytically. The effects of intrinsic decoherence and detuning strength on the dynamics of bipartite entropic uncertainty, purity and entanglement are explored. Depending on the field parameters, nonclassical correlations can be preserved. Nonclassical correlations and revival aspects appear to be significantly inhibited when intrinsic decoherence increases. Nonclassical correlations stay longer and have greater revivals due to the high detuning of the two qubits and the coherence strength of the initial cavity fields. Quantum memory-assisted entropic uncertainty and entropy have similar dynamics while the negativity presents fewer revivals in contrast.

Protonation states at different pH, conformational changes and impact of glycosylation in synapsin Ia.

Synapsin I (SynI) is the most abundant brain phosphoprotein present at presynaptic terminals that regulates neurotransmitter release, clustering of synaptic vesicles (SVs) at active zones, and stimulates synaptogenesis and neurite outgrowth. Earlier studies have established that SynI displays pH-dependent tethering of SVs to actin filaments and exhibits a maximum binding around neutral pH, however, the effect of pH shift from acidic to basic on the conformational stability of SynI has not been explored yet. Another important aspect of SynIa is its O-GlcNAcylation (O-GlcNac) at the Thr87 position, which is responsible for the positive regulation of synaptic plasticity linked to learning and memory in mice. Furthermore, reduced levels of O-GlcNAc have been observed in Alzheimer's disease, suggesting a possible link to deficits in synaptic plasticity. In this study, the effect of pH and glycosylation on the structure and functional stability of SynIa is determined through molecular dynamics (MD) simulation approach. The 3D structure of SynIa was established via threading-based homology modeling methods. It was observed that the structure of SynIa adopts extended conformational changes as the pH shifts from acidic to basic, resulting in a compact conformation at pH 8.0. Moreover, the results obtained by comparing the glycosylated and unglycosylated protein indicated that the glycan moiety imparts stability to the protein by forming intramolecular hydrogen bond interactions with the protein residues. The results indicate that although O-GlcNAc moieties do not induce a significant change in SynIa structure they minimize protein dynamics, likely leading to enhanced protein stability.

Dibenzocyclooctadiene lignans from Kadsura coccinea alleviate APAP-induced hepatotoxicity via oxidative stress inhibition and activating the Nrf2 pathway in vitro.

Phytochemical investigation on the roots of Kadsura coccinea led to the isolation five previously unknown dibenzocyclooctadiene lignans, named heilaohusuins A-E (1-5). Their structures determined by NMR spectroscopy, HR-ESI-MS, and ECD spectra. Hepatoprotection effects of a series of dibenzocyclooctadiene derivatives (1-68) were investigated against acetaminophen (APAP) induced HepG2 cells. Compounds 2, 10, 13, 21, 32, 41, 46, and 49 showed remarkable protective effects, increasing the viabilities to > 52.2% (bicyclol, 52.1 ± 1.3%) at 10 µM. The structure-activity relationships (SAR) for hepatoprotective activity were summarized, according to the activity results of dibenzocyclooctadiene derivatives. Furthermore, we found that one new dibenzocyclooctadiene lignan heilaohusuin B attenuates hepatotoxicity, the mechanism might be closely correlated with oxidative stress inhibition via activating the Nrf2 pathway.

Spatial appraisal of flood risk assessment and evaluation using integrated hydro-probabilistic approach in Panjkora River Basin, Pakistan.

Globally, flood is one of the devastating hydrometeorological disasters, causing human losses and damages to properties and infrastructure. There is a need to determine and geo-visualize flood risk to assist decision-making process for flood risk reduction. The current study is a local level pioneering attempt regarding the spatial appraisal of flood risk assessment and evaluation in Panjkora River Basin, eastern Hindu Kush. An integrated hydro-probabilistic approach is implemented by clubbing the results of Hydrologic Engineering Centre's River Analysis System (HEC-RAS) and Hydrologic Engineering Centre's Geographic River Analysis System (HEC-Geo-RAS) in geographic information system (GIS) environment. An Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is used as input data to delineate the target basin and generation of river geometry. Hydraulic and hydrological data were used to estimate and geo-visualize vertical profile and spatial extent of various floods in the active floodplain of Panjkora River. Gumbel's frequency distribution model is applied in analyzing daily peak discharge recorded during the past 32 years, and 200-year flood magnitude (1392m3/sec), probable inundation (45.5 km2), and vertical profile (19 m) are modeled. Analysis revealed that likelihood of such flood has increased the risk of potential damages to roads (46 km), retaining walls (49 km), bridges (16), and culverts (46). The analysis further revealed that built-up area (10.4 km2) and agricultural land (20.2 km2) will also come under flood with life loss. The resultant flood risk zones and spatial appraisal will definitely help in bringing down the probable flood damages. Similarly, current study has potential to assist disaster managers, hydraulic engineers, and policy makers to understand the flood risk and implement location-specific effective flood risk reduction strategies.

Analysis and forecasting of municipal solid waste in Nankana City using geo-spatial techniques.

The objective of this study was to analyze and forecast municipal solid waste (MSW) in Nankana City (NC), District Nankana, Province of Punjab, Pakistan. The study is based on primary data acquired through a questionnaire, Global Positioning System (GPS), and direct waste sampling and analysis. Inverse distance weighting (IDW) technique was applied to geo-visualize the spatial trend of MSW generation. Analysis revealed that the total MSW generated was 12,419,636 kg/annum (12,419.64 t) or 34,026.4 kg/day (34.03 t), or 0.46 kg/capita/day (kg/cap/day). The average wastes generated per day by studied households, clinics, hospitals, and hotels were 3, 7.5, 20, and 15 kg, respectively. The residential sector was the top producer with 95.5% (32,511 kg/day) followed by commercial sector 1.9% (665 kg/day). On average, high-income and low-income households were generating waste of 4.2 kg/household/day (kg/hh/day) and 1.7 kg/hh/day, respectively. Similarly, large-size families were generating more (4.4 kg/hh/day) waste than small-size families (1.8 kg/hh/day). The physical constituents of MSW generated in the study area with a population of about 70,000 included paper (7%); compostable matter (61%); plastics (9%); fine earth, ashes, ceramics, and stones (20.4%); and others (2.6%).The spatial trend of MSW generation varies; city center has a high rate of generation and towards periphery generation lowers. Based on the current population growth and MSW generation rate, NC is expected to generate 2.8 times more waste by the year 2050.This is imperative to develop a proper solid waste management plan to reduce the risk of environmental degradation and protect human health. This study provides insights into MSW generation rate, physical composition, and forecasting which are vital in its management strategies.

Ultrasonic synthesis of tyramine derivatives as novel inhibitors of α-glucosidase in vitro.

Tyramine derivatives 3-27 were synthesized by using conventional and environmental friendly ultrasonic techniques. These derivatives were then evaluated for the first time for their α-glucosidase (Sources: Saccharomyces cerevisiae and mammalian rat-intestinal acetone powder) inhibitory activity by using in vitro mechanism-based biochemical assays. Compounds 7, 14, 20, 21 and 26 were found to be more active (IC50 = 49.7 ± 0.4, 318.8 ± 3.7, 23.5 ± 0.9, 302.0 ± 7.3 and 230.7 ± 4.0 µM, respectively) than the standard drug, acarbose (IC50 = 840.0 ± 1.73 µM (observed) and 780 ± 0.028 µM (reported)) against α-glucosidase obtained from Saccharomyces cerevisiae. Kinetic studies were carried out on the most active members of the series in order to determine their mode of inhibition and dissociation constants. Compounds 7, 20 and 26 were found to be the competitive inhibitors of α-glucosidase. These compounds were also screened for their protein antiglycation, and dipeptidyl peptidase-IV (DPP-IV) inhibitory activities. Only compounds 20, 22 and 27 showed weak antiglycation activity with IC50 values 505.27 ± 5.95, 581.87 ± 5.50 and 440.58 ± 2.74 µM, respectively. All the compounds were found to be inactive against DDP-IV enzyme. Inhibition of α-glucosidase, DPP-IV enzymes and glycation of proteins are valid targets for the discovery of antidiabetic drugs. Cytotoxicity of compounds 3-27 was also evaluated by using mouse fibroblast 3T3 cell lines. All the compounds were found to be noncytotoxic. The current study describes the synthesis α-glucosidase inhibitory activity of derivatives, based on a natural product tyramine template. The compounds reported here may serve as the starting point for the design and development of novel α-glucosidase inhibitors as antidiabetic agents.

Enhancing heart disease prediction using a self-attention-based transformer model.

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Threatening language detection from Urdu data with deep sequential model.

The Urdu language is spoken and written on different social media platforms like Twitter, WhatsApp, Facebook, and YouTube. However, due to the lack of Urdu Language Processing (ULP) libraries, it is quite challenging to identify threats from textual and sequential data on the social media provided in Urdu. Therefore, it is required to preprocess the Urdu data as efficiently as English by creating different stemming and data cleaning libraries for Urdu data. Different lexical and machine learning-based techniques are introduced in the literature, but all of these are limited to the unavailability of online Urdu vocabulary. This research has introduced Urdu language vocabulary, including a stop words list and a stemming dictionary to preprocess Urdu data as efficiently as English. This reduced the input size of the Urdu language sentences and removed redundant and noisy information. Finally, a deep sequential model based on Long Short-Term Memory (LSTM) units is trained on the efficiently preprocessed, evaluated, and tested. Our proposed methodology resulted in good prediction performance, i.e., an accuracy of 82%, which is greater than the existing methods.

A novel anticonvulsant modulates voltage-gated sodium channel inactivation and prevents kindling-induced seizures.

Here, we explore the mechanism of action of isoxylitone (ISOX), a molecule discovered in the plant Delphinium denudatum, which has been shown to have anticonvulsant properties. Patch-clamp electrophysiology assayed the activity of ISOX on voltage-gated sodium channels (VGSCs) in both cultured neurons and brain slices isolated from controls and rats with experimental epilepsy(kindling model). Quantitative transcription polymerase chain reaction (qRT-PCR) (QPCR) assessed brain-derived neurotrophic factor (BDNF) mRNA expression in kindled rats, and kindled rats treated with ISOX. ISOX suppressed sodium current (I(Na)) showing an IC50 value of 185 nM in cultured neurons. ISOX significantly slowed the recovery from inactivation (ISOX τ = 18.7 ms; Control τ = 9.4 ms; p < 0.001). ISOX also enhanced the development of inactivation by shifting the Boltzmann curve to more hyperpolarized potentials by -11.2 mV (p < 0.05). In naive and electrically kindled cortical neurons, the IC50 for sodium current block was identical to that found in cultured neurons. ISOX prevented kindled stage 5 seizures and decreased the enhanced BDNF mRNA expression that is normally associated with kindling (p < 0.05). Overall, our data show that ISOX is a potent inhibitor of VGSCs that stabilizes steady-state inactivation while slowing recovery and enhancing inactivation development. Like many other sodium channel blocker anti-epileptic drugs, the suppression of BDNF mRNA expression that usually occurs with kindling is likely a secondary outcome that nevertheless would suppress epileptogenesis. These data show a new class of anti-seizure compound that inhibits sodium channel function and prevents the development of epileptic seizures.

New Records of Ditylenchus Species from Southern Alberta, Canada.

The presence of plant-parasitic nematodes (PPNs) in cultivated areas is a limiting factor in achieving marketable crop yield. To control and alleviate the effects of these nematodes and determine appropriate management strategies, species-level identification is crucial. Therefore, we conducted a nematode diversity survey, which resulted in the detection of four Ditylenchus species in cultivated areas of southern Alberta, Canada. The recovered species had six lines in the lateral field, delicate stylets (>10 µm long), distinct postvulval uterine sacs, and pointed to rounded tail tips. The morphological and molecular characterization of these nematodes revealed their identity as D. anchilisposomus, D. clarus, D. tenuidens and D. valveus, all of which are members of the D. triformis group. All of the identified species were found to be new records in Canada except for D. valveus. Accurate Ditylenchus species identification is crucial because false-positive identification can result in the implementation of quarantine measures over the detected area. Our current study not only documented the presence of Ditylenchus species from southern Alberta, but also described their morpho-molecular characteristics and subsequent phylogenetic relationships with related species. The results of our study will aid in the decision on whether these species should become a part of nematode management programs since nontarget species can become pests due to changes in cropping patterns or climate.

Extremal quantum correlation generation using a hybrid channel.

The preservation of quantum correlations requires optimal procedures and the proper design of the transmitting channels. In this regard, we address designing a hybrid channel comprising a single-mode cavity accompanied by a super-Gaussian beam and local dephasing parts based on the dynamics of quantum characteristics. We choose two-level atoms and various functions such as traced-distance discord, concurrence, and local-quantum uncertainty to analyze the effectiveness of the hybrid channel to preserve quantum correlations along with entropy suppression discussed using linear entropy. The joint configuration of the considered fields is found to not only preserve but also generate quantum correlations even in the presence of local dephasing. Most importantly, within certain limits, the proposed channel can be readily regulated to generate maximal quantum correlations and complete suppression of the disorder. Besides, compared to the individual parts, mixing the Fock state cavity, super-Gaussian beam, and local dephasing remains a resourceful choice for the prolonged quantum correlations' preservation. Finally, we present an interrelationship between the considered two-qubit correlations' functions, showing the deviation between each two correlations and of the considered state from maximal entanglement under the influence of the assumed hybrid channel.

Comparative study of quantum Otto and Carnot engines powered by a spin working substance.

Quantum Otto and Carnot engines have recently been receiving attention due to their ability to achieve high efficiencies and powers based on the laws of quantum mechanics. This paper discusses the theory, progress, and possible applications of quantum Otto and Carnot engines, such as energy production, cooling, and nanoscale technologies. In particular, we investigate a two-spin Heisenberg system that works as a substance in quantum Otto and Carnot cycles while exposed to an external magnetic field with both Dzyaloshinsky-Moriya and dipole-dipole interactions. The four stages of engine cycles are subject to analysis with respect to the heat exchanges that occur between the hot and cold reservoirs, alongside the work done during each stage. The operating conditions of the heat engine, refrigerator, thermal accelerator, and heater are all achieved. Moreover, our results demonstrate that the laws of thermodynamics are strictly upheld and the Carnot cycle produces more useful work than that of the Otto cycle.

Three new constituents from the Tujia ethnomedicine Swertia punicea Hemsl.

Three new constituents: 1,5R-dihydroxy-3,8S-dimethoxy-5,6,7,8-tetrahydroxanthone (1), (3S,4R,16S,17R)-3,16,23-trihydroxyoleana-11,13(18)-dien-28-aldehyde-3-O-ß-D-glucopyranoside (2), and new natural product (S)-gentiandiol (3), along with 41 known compounds were isolated from Tujia ethnomedicine Shuihuanglian, namely, the whole plant of Swertia punicea. Structures of all these compounds were established through extensive spectroscopic techniques, namely 1D, 2D-NMR spectroscopy, HRESIMS analysis, and the absolute configuration of the new compounds was discerned by circular dichroism (CD) spectroscopy. Antioxidative effects of these compounds were evaluated by using the DPPH radical scavenging method, compounds 7, 9 and 14 showed antioxidant activities with IC50 values of 68.9, 50.8 and 48.2 µM, respectively.