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Background: Trans-cerebellar diameter (TCD) is a crucial fetal biometric parameter used in modern radiology to assess the progress of fetal growth which is vital for prosperous maternal and fetal well-being. Despite advances in ultrasound technology, the accurate measurement of fetal biometers continues to pose considerable challenges. TCD provides a more accurate gestational age, especially in late pregnancy when other parameters may be less reliable due to organic conditions. Aims and Objectives: The aim of this study was to evaluate the significance of TCD as a reliable biometric parameter in estimating gestational age and to measure the TCD in different gestational ages and establish a reference range. Assessing the correlation between TCD and other biometric parameters to that of gestational age. Materials and Methods: A cross-sectional study was conducted at SKIMS MCH Bemina, with 60 pregnant female participants recruited in the study, with consecutive purposive sampling used in the study. Consent was obtained with ethical clearance sought. The correlation between TCD, biparietal diameter (BPD), and crown-rump length (CRL) with gestational age was evaluated using the Statistical Package for the Social Sciences 24.0 to determine the strength of these relationships. Results: The study found strong correlations between fetal biometric measurements and gestational age: TCD (r=0.995), BPD (r=0.993), and CRL (r=0.988). A linear increase in BPD and CRL with increasing gestational age was observed, consistent with previous research the TCD was found particularly as a strong reliable parameter with a higher correlation for assessing fetal growth, especially in cases where other measurements may be less reliable. Conclusion: Findings suggest that TCD could be a valuable addition to standard fetal biometric measurements in clinical practice. The strong correlation between TCD and gestational age has pivotal implications for improving gestational age estimation. More research is needed to emphasize the use of TCD across many population samples and to explore its potential in fetal growth assessment.

Abstract Bitumen shows visco-elastic behavior, exhibiting both elastic and viscous properties as predicted by dynamic response and phase angle. Modern asphalt bituminous pavements face issues such as early-stage fatigue cracks, rutting, and permanent deformations due to low-temperature cracking, high-temperature deformation, moisture susceptibility, and overloading. These pavement distresses result in the formation of potholes, alligator cracks, and various deformations, which accelerate the need for rehabilitation and maintenance. To address these concerns, this study focused on utilizing Nitrile Butadiene Rubber derived from surgical gloves as an additive in conventional asphalt pavements to assess its effect on stiffness. Nitrile Butadiene Rubber was added in intervals of 2%, 4%, 6%, and 8% to conventional bituminous pavement. The rheological properties, marshall properties, dynamic modulus, and phase angle were evaluated for varying percentages of Nitrile Butadiene Rubber at different temperature, and frequency. The dynamic response was determined using a simple performance tester at four different temperatures (4.4 °C, 21.1 °C, 37.8 °C, and 54.4 °C) and six different frequencies (0.1, 0.5, 1, 5, 10, and 25 Hz). Response surface methodology was employed to establish a relationship between input and output variables and to optimize the amount of Nitrile Butadiene Rubber in the mix based on dynamic modulus and phase angle. The study concluded that adding up to 6% of Nitrile Butadiene Rubber improved Marshall stability, while higher percentages led to reduced stability. A similar trend was observed in the dynamic modulus, which peaked with the addition of 6% Nitrile Butadiene Rubber, regardless of frequency and temperature. The response surface methodology model indicated that coupling the percentage of Nitrile Butadiene Rubber with frequency increased the dynamic modulus at a constant temperature, with the highest value occurring at 4.4 °C. However, the dynamic modulus decreased as the temperature rose for the same combinations of Nitrile Butadiene Rubber percentages and frequencies. Numerical optimization suggested that a maximum of 5.9% Nitrile Butadiene Rubber should be added to achieve the highest dynamic modulus and lowest phase angle.

This study explores a novel approach utilizing acoustic emission (AE) signaling technology for pipeline leakage detection and analysis. Pipeline leaks are a significant concern in the liquids and gases industries, prompting the development of innovative detection methods. Unlike conventional methods, which often require contact and visual inspection with the pipeline surface, the proposed time-series-based deep learning approach offers real-time detection with higher safety and efficiency. In this study, we propose an automatic detection system of pipeline leakage for efficient transportation of liquid (water) and gas across the city, considering the smart city approach. We propose an AE-based framework combined with time-series deep learning algorithms to detect pipeline leaks through time-series features. The time-series AE signal detection module is designed to capture subtle changes in the AE signal state caused by leaks. Sequential deep learning models, including long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and gated recurrent units (GRUs), are used to classify the AE response into normal and leakage detection from minor seepage, moderate leakage, and major ruptures in the pipeline. Three AE sensors are installed at different configurations on a pipeline, and data are acquired at 1 MHz sample/sec, which is decimated to 4K sample/second for efficiently utilizing the memory constraints of a remote system. The performance of these models is evaluated using metrics, namely accuracy, precision, recall, F1 score, and convergence, demonstrating classification accuracies of up to 99.78%. An accuracy comparison shows that BiLSTM performed better mostly with all hyperparameter settings. This research contributes to the advancement of pipeline leakage detection technology, offering improved accuracy and reliability in identifying and addressing pipeline integrity issues.

Abstract This study evaluated the impact of differential sowing windows and improved weed management strategies on weed dynamics, productivity, and economic viability of direct drum seeded rice (Oryza sativa L.) in the temperate agro-ecosystem of Kashmir. A two-year field experiment was conducted utilizing a split-plot design with two sowing dates (May 10 and June 3) as main plots and six weed management practices as sub-plots. The earlier sowing date (May 10) resulted in significantly enhanced leaf area index, crop growth rate, relative growth rate, net assimilation rate, and grain and straw yields compared to the later sowing (June 3). Among weed management treatments, four mechanized conoweedings (equivalent to weed-free conditions) and sequential application of bensulfuron methyl + pretilachlor (60 and 600 g a.i. ha−1) as pre-emergence followed by 2,4-D (0.75 kg a.i. ha−1) as post-emergence demonstrated superior efficacy in weed suppression and augmentation of crop growth parameters and yield attributes. These treatments also exhibited the lowest weed index and highest benefit–cost ratio. The May 10 sowing, coupled with efficacious weed control measures, significantly reduced weed density and biomass while concomitantly improving nutrient uptake and economic returns. The results indicate that adopting a May 10 sowing date for direct seeded rice, in conjunction with either four conoweedings or the aforementioned sequential herbicide application, can optimize agronomic productivity and economic profitability under the temperate conditions of Kashmir. The study aided in choosing the best sowing window and efficient weed management strategy for attaining higher productivity and profitability of direct seeded rice in temperate conditions.

Abstract Background This study aimed to evaluate the suitability of using drain water as a source of irrigation and its effects along with salicylic acid on morphological, anatomical, physico-chemical as well as yield attributes of potato. For this study, potato tubers were grown in pots and irrigated with different concentrations of drain water. Salicylic acid treatments vis. 0, 0.5 and 1.0 mM were applied foliarly. Pre- and post-harvest analysis was carried out to determine different attributes of soil, water and plants after 60 days. Results The growth of potato plant was increased as the concentration of SA increased through increasing shoot length, fresh/dry weight and tuber number/plant. In this research work, plant respond to overcome metal stresses by up regulating antioxidant defense system such as, peroxidase, catalase and superoxide dismutase) by application of highest treatment of SA when irrigated with 6% drain water. Plants accumulated the highest concentrations of Cd, Cr, and Pb in the leaves when treated with 1 mM of SA, compared to other plant parts. It was observed that photosynthetic pigment enhanced in 6% drain water treated plants when applied with 1mM SA as compared to control. An increase in epidermis and cortical cell thickness, as well as stomatal closure, was observed, helping to maintain water loss under stress conditions. Conclusions According to these results, it can be suggested that SA is potent signaling molecule can play an essential role in maintaining potato growth when irrigated with drain water containing heavy metals through stimulating metal up take and up regulation of antioxidant enzymes.

Abstract Apple, a vital fruit crop worldwide and a major crop grown in northern parts of India, acts as a backbone for the survival and livelihood of the farming community. However, it is prone to severe damage from insect pests and diseases. In the past few years, due to erratic weather patterns, there has been an alarmingly increased infestation of different insect pests, both invasive and non-invasive, resulting in substantial economic losses to this industry. One similar case was seen in the Himalayan region of India, where the apple crop in Karewas was heavily damaged by some invasive fruit borer, feeding on pulp and making galleries to reach the seed locules, thereby destroying the seed material. To ascertain the true identity of this pest, a study based on morpho-molecular characterization of this pest was carried out in the Faculty of Agriculture, Wadura, SKUAST Kashmir, India, during the years 2021 and 2022. The invasive fruit samples were collected from apple orchards at different locations (experimental sites) in North India by installing delta sticky traps @ 5 traps/ha for moth collection. The fruit-boring larvae and pupa were also collected and reared in confined chambers of the laboratory with controlled temperature and humidity. All the laboratory investigations were conducted at the Division of Entomology, FoA, Wadura, SKUAST Kashmir. During the investigation, it was confirmed that the invasive borer is a codling moth (Cydia pomonella L.), a member of the family Tortricidae, order Lepidoptera, which was earlier having quarantine importance in India, as it was only present in the Ladakh region of India. From the phylogenetic analysis of sample sequences, the species of codling moth present at all experimental sites was more identical to the codling moth sequence from Leh (the northernmost arid region), India. Further, the study of life cycle and voltinism revealed that codling moth completes three generations per year in Kashmir, with a single cycle lasting up to 2.5 months. However, the timing of various generations varies, depending on prevailing weather conditions at specific locations and times. The number of generations completed by codling moth in Kashmir conditions during apple growing season was three full and a half overwintering generations. The study of the life cycle of this important pest was necessary to know the weak link for its effective management and to prevent the economic loss in apple fruit.

Background: Infertility is a prevalent medical issue that can arise from challenges in male or female or both partners. Pivotal contributing factors to female infertility include dysfunction, tubal issues, and peritoneal factors. Hysterosalpingography (HSG) holds significant importance in assessing abnormalities associated with the uterus and fallopian tubes. Aims and Objectives: This study aimed to assess the clinical profile and hysterosalpingographic findings of women with infertility who visited the department of radiodiagnosis for HSG. Materials and Methods: This is a cross-sectional study, where consecutive convenience-based sampling was used, with ethical clearance in place. Sixty-three infertile women of reproductive age were recruited for the study and were referred for HSG by the Department of Obstetrics and Gynecology. Results: The majority of the participants had primary infertility (71.88%) and was in the 31–35-year age group with the majority having 3–6 years of infertility and endometriosis. On HSG uterine pathology was found in 9.37% of participants and unilateral and bilateral tubal blocks were seen in 18.75% and 14.06% of cases approximately. Conclusion: HSG is a minimally invasive procedure, secure, and plays a crucial role in diagnosing uterine and tubal factors contributing to infertility.

Glaciers in the Karakoram region of Pakistan are crucial water sources for downstream communities, supporting irrigation and power generation. Global warming has led to significant glacier mass loss and size reduction, posing risks to ecology, climate, and water resources. Understanding Glacier Mass Balance (GMB) is essential for climate change insights and predicting future trends, mainly due to limited long-term GMB and Equilibrium Line Altitude (ELA) data from the Karakoram region. We employed hypsometrically controlled methods, Accumulation Area Ratio (AAR) and Accumulation Area Balance Ratio (AABR) to fill this gap. AAR values ranged from 0.4 to 0.8 at 0.5 intervals, and AABR from 0.9 to 2.2 at 0.01 intervals. Findings indicate that Shisper Glacier had a negative mass balance of –1.0 m/y w.e., with the area below ELAs expanding by 1.72 km². Muchuhar Glacier showed a negative mass balance of -0.05 m/y w.e., with the area below ELAs decreasing to 0.96 km². Surface temperature trends indicate 6.44°C in 2023, highlighting a warming trend. Rising average air temperature (Tavg) is the significant factor influencing ELAs (26% and 72%, respectively), followed by average precipitation (Pavg) at 20% and 26%. These findings highlight the critical role of temperature and precipitation in glacier mass balance.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

ObjectivesTo investigate the unique challenges faced by older women during the COVID-19 pandemic regarding social connection, feelings of loneliness, and their subsequent impact on mental health and well-being.MethodA qualitative research methodology is used to examine how older women experienced loneliness, social relationships, and mental health consequences during the COVID-19 pandemic.ResultsThe results are discussed in terms of two main themes each with their sub-themes; 1. Social Connection amidst Physical Distancing Measures, 2. Impact on Mental Health and Well-being.ConclusionThe study highlights the significant impact of loneliness and social isolation on the mental health of older women during the COVID-19 pandemic, emphasizing the need for targeted interventions and support systems.

Modern agriculture has encountered several challenges in achieving constant yield stability especially due to disease outbreaks and lack of long-term disease-resistant crop cultivars. In the past, disease outbreaks in economically important crops had a major impact on food security and the economy. On the other hand climate-driven emergence of new pathovars or changes in their host specificity further poses a serious threat to sustainable agriculture. At present, chemical-based control strategies are frequently used to control microbial pathogens and pests, but they have detrimental impact on the environment and also resulted in the development of resistant phyto-pathogens. As a replacement, cultivating engineered disease-resistant crops can help to minimize the negative impact of regular pesticides on agriculture and the environment. Although traditional breeding and genetic engineering have been instrumental in crop disease improvement but they have certain limitations such as labour intensity, time consumption, and low efficiency. In this regard, genome editing has emerged as one of the potential tools for improving disease resistance in crops by targeting multiple traits with more accuracy and efficiency. For instance, genome editing techniques, such as CRISPR/Cas9, CRISPR/Cas13, base editing, TALENs, ZFNs, and meganucleases, have proved successful in improving disease resistance in crops through targeted mutagenesis, gene knockouts, knockdowns, modifications, and activation of target genes. CRISPR/Cas9 is unique among these techniques because of its remarkable efficacy, low risk of off-target repercussions, and ease of use. Some primary targets for developing CRISPR-mediated disease-resistant crops are host-susceptibility genes (the S gene method), resistance genes (R genes) and pathogen genetic material that prevents their development, broad-spectrum disease resistance. The use of genome editing methods has the potential to notably ameliorate crop disease resistance and transform agricultural practices in the future. This review highlights the impact of phyto-pathogens on agricultural productivity. Next, we discussed the tools for improving disease resistance while focusing on genome editing. We provided an update on the accomplishments of genome editing, and its potential to improve crop disease resistance against bacterial, fungal and viral pathogens in different crop systems. Finally, we highlighted the future challenges of genome editing in different crop systems for enhancing disease resistance.

Waterlogging is a constant threat to crop productivity and ecological biodiversity. Plants face multiple challenges during waterlogging stress like metabolic reprogramming, hypoxia, nutritional depletion, reduction in gaseous exchange, pH modifications, microbiome alterations and disease promotion all of which threaten plants survival. Due to global warming and climatic change, the occurrence, frequency and severity of flooding has dramatically increased posing a severe threat to food security. Thus, developing innovative crop management technologies is critical for ensuring food security under changing climatic conditions. At present, the top priority among scientists is to find nature-based solutions to tackle abiotic or biotic stressors in sustainable agriculture in order to reduce climate change hazards to the environment. In this regard, utilizing plant beneficial microbiome is one of the viable nature based remedial tool for mitigating abiotic stressors like waterlogging. Beneficial microbiota provides plants multifaceted benefits which improves their growth and stress resilience. Plants recruit unique microbial communities to shield themselves against the deleterious effects of biotic and abiotic stress. In comparison to other stressors, there has been limited studies on how waterlogging stress affects plant microbiome structure and their functional traits. Therefore, it is important to understand and explore how waterlogging alters plant microbiome structure and its implications on plant survival. Here, we discussed the effect of waterlogging stress in plants and its microbiome. We also highlighted how waterlogging stress promotes pathogen occurrence and disease development in plants. Finally, we highlight the knowledge gaps and areas for future research directions on unwiring how waterlogging affects plant microbiome and its functional traits. This will pave the way for identifying resilient microbiota that can be engineered to promote their positive interactions with plants during waterlogging stress.

Pressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. Monitoring the condition and predicting the remaining useful life before the failure of pressurized pipes are essential for informed and timely maintenance decisions. In this work, we propose a novel method for predicting the remaining useful life of pressurized pipelines based on acoustic emission monitoring and similarity-based learning. Specifically, acoustic emission sensors are deployed to record acoustic emission events caused by cracks in the pipeline. A pipeline health indicator is proposed based on accumulated events detected through a constant false alarm rate signal detector. Leveraging the historical run-to-fail trajectories of the health indicator, a similarity measure is introduced to predict the remaining pipeline life. This method computes the similarity between the current health indicator trajectory and past trajectories based on a Euclidean distance in the proposed derivative convolutional domain. Trajectory similarity determines the remaining lifetime similarity, which is weighted using data-adaptive fuzzy rules to estimate the current remaining useful life. Elaborate experimental validations are conducted on a custom pressurized pipeline system in a laboratory setting. Experimental results demonstrate the high efficacy of the proposed method in predicting the remaining useful life of the pipeline, surpassing other commonly used methods in both accuracy and certainty.

Background: The prevalence of cancers differs in different parts of the world and which makes studying every population important to know various risk factors associated with cancers common in various parts of the world. This study was undertaken to know the burden of cancer in rural Kashmir. Materials and Methods: Cases registered in the Department of Radiation Oncology, GMC, Anantnag, during 2019–2020 were enrolled for this study to check the prevalence and profile of cancer. Results: Stomach cancer was the most common cancer diagnosed in 233 patients in 2019–20 overall. In males, it was seconded by esophageal cancer, whereas in females, breast cancer was the second-most common cancer. Conclusion: Patterns of cancer distribution differ vastly across India; cancer patterns in Kashmir are more similar to the Caspian cancer belt with upper gastrointestinal tract cancer dominating both sexes.

Background: Prescription audit can be described as a tool to evaluate the quality of medical treatment which is being offered to the patients in any health care facility. It can prove to be helpful in devising any effective changes that would improve quality of care to the patients. Aims and Objective: The study was conducted to carry out prescription audit among the psychiatric patients seeking consultation in a tertiary care centre. Materials and Methods: This prospective observational study was conducted on 500 prescriptions for a time period of six months in the outpatient department of Institute of Mental Health and Neurosciences (MHANS). During the study all the prescriptions were collected and evaluated based on WHO prescribing indicators. Data was entered and analyzed using SPSS software version 16.0. Results: Five hundred prescriptions comprising of 1,260 drugs were analyzed. The average number of drugs per prescription was 2.8. There were 52.7% males and 47.3% females. Most patients (49%) were from the age group of 21-40 years. In majority of the cases (90.9%) more than one drug was prescribed and 4.6% (20) of medications were prescribed by generic names. The study revealed that Major Depression Disorder (MDD) was the most common psychiatric morbidity (40%). Clonazepam was the most common drug (19.6%) that was prescribed for the patients irrespective of the diagnosis. Conclusion: Prescription audit can be helpful to evaluate the existing prescription pattern of any health care facility and ensure the rational drug therapy as per WHO prescription parameters.

Abstract Apple cankers are extremely destructive diseases threatening the global apple industry through direct and indirect losses. The population structure of the pathogens is of paramount significance for the development of efficient management strategies. Therefore, phenotypic, pathogenic, and genetic diversity of Diplodia seriata causing black rot canker of apple was investigated in this study. All the isolates were included for investigating the in vitro mycelial growth, conidial dimensions, and pathogenic variability on two-year-old potted apple seedlings. The ISSR approach was used to investigate the molecular diversity of D. seriata. Mycelial growth rates were found to vary significantly amongst the isolates; however, there were no major variations seen between the different geographical groupings of isolates. Pathogenicity tests revealed variations in the size of cankers among the isolates indicating the presence of virulence variability. The isolates were segregated into three virulence groups based on canker length. The Bayesian analyses of ISSR data divided the isolates into two genetic clusters. The genetic clustering of the isolates revealed no relationship with geographical origin of the isolates. Furthermore, no direct relationship of genetic clustering was observed with morphological or pathogenic variability. The ISSR primers revealed very high level of variability in D. seriata; however, no distinct populations of the pathogen existed which is an indication of high level of gene flow between the diverse geographical populations. According to our knowledge, this is the first thorough investigation on the diversity of D. seriata associated with apple black rot canker in India.

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach’s durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims.

Horticultural crops are vulnerable to diverse microbial infections, which have a detrimental impact on their growth, fruit quality, and productivity. Currently, chemical pesticides are widely employed to manage diseases in horticultural crops, but they have negative effects on the environment, human health, soil physiochemical properties, and biodiversity. Additionally, the use of pesticides has facilitated the development and spread of resistant pathovars, which have emerged as a serious concern in contemporary agriculture. Nonetheless, the adverse consequences of chemical pesticides on the environment and public health have worried scientists greatly in recent years, which has led to a switch to the use of biocontrol agents such as bacteria, fungi, and insects to control plant pathogens. Biocontrol agents (BCAs) form an integral part of organic farming, which is regarded as the future of sustainable agriculture. Hence, harnessing the potential of BCAs is an important viable strategy to control microbial disease in horticultural crops in a way that is also ecofriendly and can improve the soil health. Here, we discuss the role of the biological control of microbial diseases in crops. We also discuss different microbial-based BCAs such as fungal, bacterial, and viral and their role in disease management. Next, we discuss the factors that affect the performance of the BCAs under field conditions. This review also highlights the genetic engineering of BCAs to enhance their biocontrol efficiency and other growth traits. Finally, we highlight the challenges and opportunities of biocontrol-based disease management in horticulture crops and future research directions to boost their efficacy and applications.

Turmeric has attracted a significant amount of interest in recent years due to its strong antimicrobial properties. The tissue culture of turmeric is preferred to obtain disease-free, highest number of plantlets with good uniform chemistry. However, there is a need to increase the speed of the whole process to meet the growing demand for planting materials and to save time and resources. Iron oxide nanoparticles (Fe3O4 NPs) showed positive effects on callus initiation time, proliferation rate, percent root response, shoot length, percent rooting, and number of roots per explant. Highest callus induction, i.e., 80%, was recorded in cultures that were grown in the presence of 15 mg/L of Fe3O4 NPs. Callus initiated earlier in culture tubes that received green synthesized iron nanoparticles in a concentration between 10–15 mg/L. Biofabricated nanoparticles were characterized for their size, physiochemical, and optical properties through UV–Vis spectroscopy, FTIR, XRD, and SEM. Curcuminoids profiling was performed by implementing LC-Ms that revealed increased quantities in plantlets grown in nano-supplemented media when compared to the control.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Stevia rebaudiana Bert. is commonly known as candy leaf, sugar leaf, or sweet leaf. It is a natural sweetener that has low calories and is used as a substitute for sucrose. The objective of this research is to evaluate the effects of graphene oxide (GO) on the growth, biochemical activities, and stevioside and rebaudioside A production of Stevia in in vitro-raised plantlets. For this, green nanomaterials of GO (0, 2, 4, 6, 8, and 10 mgL−1) were applied to the in vitro plants to enhance its sweetness by triggering the production of stevioside and rebaudioside A and other growth and biochemical parameters. It was observed that all the growth parameters of Stevia plants significantly increased with all GO treatments tested. Total chlorophyll and protein contents were increased (1.85- and 2.65-fold increase from the control) by applying 8 mgL−1 of GO to the MS medium. The maximum value (4 mg·g−1 of protein) of peroxidase activity (POD) was observed by applying 4 mgL−1 of GO, 28.92-fold increase from the control. In comparison, superoxide dismutase activity (SOD) (0.4 mg·g−1 protein) was observed with 10 mgL−1 of GO (1.56-fold increase from the control). Stevioside (12.9 and 8.9 mg·g−1 DW) and rebaudioside A (3.2 and 0.81 mg·g−1 DW) were observed only at 6 and 8 mg·L−1 treatment of graphene oxide. According to the findings, using graphene oxide (GO) had a significant impact on the growth, biochemical activities, and steviol glycoside production in Stevia. This shows that GO has the potential to be a valuable enhancer of sweetness and overall Stevia leaf quality, providing great prospects for the development of low-calorie natural sweeteners.

This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from the continuous wavelet transform have been useful for pipeline health diagnosis, particularly when combined with deep learning. However, background noise has a significant impact on AE signals, which can reduce the accuracy of pipeline health identification using classification models. To address this issue, a new type of scalograms called leak-augmented scalogram is introduced, which enhances the variation in colour intensities of AE scalogram images. The leak-augmented scalograms are obtained by pre-processing them using image-enhancing Gaussian and Laplacian filters. The proposed method utilizes convolutional neural networks (CNNs) and convolutional autoencoders (CAEs) for feature extraction. The CNN extracts patterns specific to local changes, while the CAE extracts holistic patterns from the leak-augmented scalograms. The resulting leak susceptible and leak holistic indicators are merged into a single feature pool and provided as input to a shallow artificial neural network (ANN) to evaluate pipeline health conditions. The proposed method achieves high classification as well as accuracy, precision, F-1 Score and recall, compared to existing state of the art methods.

This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions.

Abstract Background Interferon and nucleos(t)ide analogues are current therapeutic treatments for chronic Hepatitis B virus (HBV) infection with the limitations of a functional cure. Chrysin (5, 7-dihydroxyflavone) is a natural flavonoid, known for its antiviral and hepatoprotective activities. However, its anti-HBV activity is unexplored. Methods In the present study, the anti-hepatitis B activity of chrysin was investigated using the in vitro experimental cell culture model, HepG2 cells. In silico studies were performed where chrysin and lamivudine (used here as a positive control) were docked with high mobility group box 1 protein (HMGB1). For the in vitro studies, wild type HBV genome construct (pHBV 1.3X) was transiently transfected in HepG2. In culture supernatant samples, HBV surface antigen (HBsAg) and Hepatitis B e antigen (HBeAg) were measured by enzyme-linked immunosorbent assay (ELISA). Secreted HBV DNA and intracellular covalently closed circular DNA (cccDNA) were measured by SYBR green real-time PCR. The 3D crystal structure of HMGB1 (1AAB) protein was developed and docked with the chrysin and lamivudine. In silico drug-likeness, Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties of finest ligands were performed by using SwissADME and admetSAR web servers. Results Data showed that chrysin significantly decreases HBeAg, HBsAg secretion, supernatant HBV DNA and cccDNA, in a dose dependent manner. The docking studies demonstrated HMGB1 as an important target for chrysin as compared to lamivudine. Chrysin revealed high binding affinity and formed a firm kissing complex with HMGB1 (∆G = − 5.7 kcal/mol), as compared to lamivudine (∆G = − 4.3 kcal/mol), which might be responsible for its antiviral activity. Conclusions The outcome of our study establishes chrysin as a new antiviral against HBV infection. However, using chrysin to treat chronic HBV disease needs further endorsement and optimization by in vivo studies in animal models. Graphical Abstract

Climate change biotic and abiotic stressors lead to unpredictable crop yield losses, threatening global food and nutritional security. In the past, traditional breeding has been instrumental in fulfilling food demand; however, owing to its low efficiency, dependence on environmental conditions, labor intensity, and time consumption, it fails to maintain global food demand in the face of a rapidly changing environment and an expanding population. In this regard, plant breeders need to integrate multiple disciplines and technologies, such as genotyping, phenotyping, and envirotyping, in order to produce stress-resilient and high-yielding crops in a shorter time. With the technological revolution, plant breeding has undergone various reformations, for example, artificial selection breeding, hybrid breeding, molecular breeding, and precise breeding, which have been instrumental in developing high-yielding and stress-resilient crops in modern agriculture. Marker-assisted selection, also known as marker-assisted breeding, emerged as a game changer in modern breeding and has evolved over time into genomics-assisted breeding (GAB). It involves genomic information of crops to speed up plant breeding in order to develop stress-resilient and high-yielding crops. The combination of speed breeding with genomic and phenomic resources enabled the identification of quantitative trait loci (QTLs)/genes quickly, thereby accelerating crop improvement efforts. In this review, we provided an update on rapid advancement in molecular plant breeding, mainly GAB, for efficient crop improvements. We also highlighted the importance of GAB for improving biotic and abiotic stress tolerance as well as crop productivity in different crop systems. Finally, we discussed how the expansion of GAB to omics-assisted breeding (OAB) will contribute to the development of future resilient crops.

Plants are challenged by different microbial pathogens that affect their growth and productivity. However, to defend pathogen attack, plants use diverse immune responses, such as pattern-triggered immunity (PTI), effector-triggered immunity (ETI), RNA silencing and autophagy, which are intricate and regulated by diverse signaling cascades. Pattern-recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat (NLR) receptors are the hallmarks of plant innate immunity because they can detect pathogen or related immunogenic signals and trigger series of immune signaling cascades at different cellular compartments. In plants, most commonly, PRRs are receptor-like kinases (RLKs) and receptor-like proteins (RLPs) that function as a first layer of inducible defense. In this review, we provide an update on how plants sense pathogens, microbe-associated molecular patterns (PAMPs or MAMPs), and effectors as a danger signals and activate different immune responses like PTI and ETI. Further, we discuss the role RNA silencing, autophagy, and systemic acquired resistance as a versatile host defense response against pathogens. We also discuss early biochemical signaling events such as calcium (Ca2+), reactive oxygen species (ROS), and hormones that trigger the activation of different plant immune responses. This review also highlights the impact of climate-driven environmental factors on host–pathogen interactions.

Plant health monitoring is essential for understanding the impact of environmental stressors (biotic and abiotic) on crop production, and for tailoring plant developmental and adaptive responses accordingly. Plants are constantly exposed to different stressors like pathogens and soil pollutants (heavy metals and pesticides) which pose a serious threat to their survival and to human health. Plants have the ability to respond to environmental stressors by undergoing rapid transcriptional, translational, and metabolic reprogramming at different cellular compartments in order to balance growth and adaptive responses. However, plants’ exceptional responsiveness to environmental cues is highly complex, which is driven by diverse signaling molecules such as calcium Ca2+, reactive oxygen species (ROS), hormones, small peptides and metabolites. Additionally, other factors like pH also influence these responses. The regulation and occurrence of these plant signaling molecules are often undetectable, necessitating nondestructive, live research approaches to understand their molecular complexity and functional traits during growth and stress conditions. With the advent of sensors, in vivo and in vitro understanding of some of these processes associated with plant physiology, signaling, metabolism, and development has provided a novel platform not only for decoding the biochemical complexity of signaling pathways but also for targeted engineering to improve diverse plant traits. The application of sensors in detecting pathogens and soil pollutants like heavy metal and pesticides plays a key role in protecting plant and human health. In this review, we provide an update on sensors used in plant biology for the detection of diverse signaling molecules and their functional attributes. We also discuss different types of sensors (biosensors and nanosensors) used in agriculture for detecting pesticides, pathogens and pollutants.

The prediction of the remaining useful life (RUL) is important for the conditions of rotating machinery to maintain reliability and decrease losses. This study proposes an efficient approach based on an adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) and a convolutional LSTM autoencoder to achieve the feature extraction, health index analysis, and RUL prediction for rotating machinery. First, the ACYCBD is used to filter noise from the vibration signals. Second, based on the peak value properties, a novel health index (HI) is designed to analyze the health conditions for the denoising signal, showing a high sensitivity for the degradation of bearings. Finally, for better prognostics and health management of the rotating machinery, based on convolutional layers and LSTM, an autoencoder can achieve a transform convolutional LSTM network to develop a convolutional LSTM autoencoder (ALSTM) model that can be applied to forecast the health trend for rotating machinery. Compared with the SVM, CNN, LSTM, GRU, and DTGRU methods, our experiments demonstrate that the proposed approach has the greatest performance for the prediction of the remaining useful life of rotating machinery.

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time–frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.

Across the globe, the frequent occurrence of drought spells has significantly undermined the sustainability of modern high-input farming systems, particularly those focused on staple crops like wheat. To ameliorate the deleterious impacts of drought through a biologically viable and eco-friendly approach, a study was designed to explore the effect of nicotinic acid on different metabolic, and biochemical processes, growth and yield of wheat under optimal moisture and drought stress (DS). The current study was comprised of different levels of nicotinic acid applied as foliar spray (0 g L−1, 0.7368, 1.477, 2.2159 g L−1) and fertigation (0.4924, 0.9848, and 1.4773 g L−1) under normal conditions and imposed drought by withholding water at anthesis stage. The response variables were morphological traits such as roots and shoots characteristics, yield attributes, grain and biological yields along with biosynthesis of antioxidants. The results revealed that nicotinic acid dose of 2.2159 g L−1 out-performed rest of treatments under both normal and DS. The same treatment resulted in the maximum root growth (length, fresh and dry weights, surface area, diameter) and shoot traits (length, fresh and dry weights) growth. Additionally, foliar applied nicotinic acid (2.2159 g L−1) also produced as the highest spike length, grains spike−1, spikelet’s spike−1 and weight of 1000 grains. Moreover, these better yield attributes led to significantly higher grain yield and biological productivity of wheat. Likewise in terms of physiological growth of wheat under DS, the same treatment remained superior by recording the highest SPAD value, relative water content, water potential of leaves, leaf area, stomatal conductance (292 mmolm−2S−1), internal carbon dioxide concentration, photosynthesis and transpiration rate. Interestingly, exogenously applied nicotinic acid remained effective in triggering the antioxidant system of wheat by recording significantly higher catalase, peroxidase, superoxide dismutase and ascorbate peroxidase.

This article examines a new linear oscillating actuator (LOA) design that uses rectangular-shaped core materials and permanent magnets (PMs). The primary objective of the paper is to examine a novel LOA topology with a rectangular-shaped core and PMs as an alternative to the tubular LOA. A static core material is housed between the mover’s components and two stator cores with two coils, each of which is placed on either side of the mover. The dimensions of all the parameters are swept for optimization and the optimal parameter dimension is chosen based on the optimal value of the electromagnetic (EM) force. An analysis is done for output results like EM force and stroke. Parameters like input current, EM force and stroke-to-current ratio are investigated for different frequencies of the input electrical loading to demonstrate the significance of resonance operation. EM force per PM mass of the investigated design of LOA is 67 percent higher than that of a conventional rectangular-shaped LOA. Additionally, the EM force density of the proposed design is 23.8 percent higher than that of the conventional design of LOA. Furthermore, the stroke of the proposed LOA is also feasible and more than most of the designs of the LOA. Additionally, the proposed design of the LOA is simple in structure, low-cost, and feasible for fabrication.

Abstract This article presents a miniature tubular moving magnet linear oscillating actuator (MT‐MMLOA) that operates on an electro dynamic actuation mechanism. The proposed linear oscillating actuator comprises two main parts: the stator and the mover. In this topology, the stator assembly is composed of a modular C‐core structure along with a separator to prevent flux cancelation, and the mover assembly accommodates axially magnetised permanent magnets (PMs) that contribute to high thrust force generation and present a less costly actuator compared to the conventional actuator design. The finite element analysis is performed and validated experimentally. Numerous linear oscillating actuator topologies proposed previously in the literature are compared with MT‐MMLOA to analyse the electromagnetic performance. The results show that the motor constant, which is the performance parameter, is significantly increased from the conventional design. The PM volume is reduced, which is cost‐effective. The overall volume of MT‐MMLOA is also reduced due to reduction in the PM volume, which is advantageous for compactness. Consequently, the proposed MT‐MMLOA is simple in structure, less costly, and easy to fabricate.

Microfinance has been evolving to be a decisive factor in poverty reduction, rural and entrepreneurial development. This research aims to find out the impact of microfinance on rural/socio-economic development and entrepreneurial skill development. Particularly, this study attempts to explore the impact of microfinance on social development, financial empowerment, education, healthcare, economic independence, and entrepreneurial development. Data was collected through questionnaires taken from the beneficiaries of the National Rural Livelihood Mission (NRLM) erstwhile Swaran Jayanti Shahri Rozgar Yojana (SJSRY) microfinance scheme in the Union Territory of Jammu and Kashmir, India. A purposive sampling method, exploratory factor analysis, and paired sample t-test were used in this study. The findings prove that microfinance facilitates social development, improved financial empowerment, and promotes decision-making relating to education and healthcare among beneficiaries. The finding also indicates that microfinance helps to increase entrepreneurial skill development among the beneficiaries. This study offers an insight how microfinance eliminates poverty and facilitates social development as well as entrepreneurial skills. These findings have essential implications for academics, policymakers, and microfinance institutions.

Abstract A Linear Oscillating Actuator (LOA) generates bi‐directional electromagnetic (EM) force between two extreme points. A new topology of moving magnet LOA with axial magnetised PMs and a C‐shaped stator core is proposed. On each side of the mover, two chamfered tubular‐shaped core material parts are attached. All the design parameters, such as mover mass and EM force of the LOA, are optimised using parametric analysis via parametric sweep, and the optimum values of the parameters are chosen based on design and performance parameters. The LOA's performance is examined for various input loading values and at different oscillation region positions. For various values of time‐dependent supply, the EM force's oscillating nature is analysed. The effect of resonance analysis on input current, stroke to current ratio, and efficiency are discussed. The proposed LOA is compared to current LOA topologies in terms of design structure and performance parameters. The FEM results are validated using a special experimental setup. The proposed LOA produces feasible results for the compressor in a refrigeration system.

Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici (Pst) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines—rust resistance line FLW29 and rust susceptible line PBW343—which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst-resistant and Pst-susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease.

Apple cultivation is one of the most significant means of subsistence in the Kashmir region of the northwestern Himalayas. It is considered as the backbone of the region's economy. Apple cultivation in the region is dominated by a late maturing cultivar “Red Delicious” which usually on maturity causes glut in the market. In order to bring new cultivars in the cultivation, and to expand the maturity season, it is necessary to evaluate the new cultivars on fruit physico-chemical attributes which ultimately decide the market rates before recommending to farmers for cultivars adoption. Therefore, the current study was carried out to evaluate thirteen apple cultivars on physico-chemical attributes over two years, 2017 and 2018 under agro-climatic conditions of Kashmir region The results revealed that cultivars differed significantly in terms of physico-chemical properties. Cultivars with the highest and lowest values for initial fruit set, fruit drop, final fruit retention, and fruit firmness in 2017 did not follow the same trend in 2018. During 2017 and 2018, cultivar Mollie's Delicious possessed the highest fruit length (72.39 mm and 81.45 mm), fruit diameter (81.18 mm and 84.14 mm), and fruit weight (205.85 g and 247.16 g), whereas cultivar Baleman's Cider had the lowest values (50.76 mm and 52.83 mm, 60.10 mm and 62.08 mm, and 71.46 g and 86.94 g), respectively. The harvesting dates were quite spread out during both years of study. Cultivar Mollie's Delicious was harvested the earliest in both years, on August 5th, 2017 and August 8th, 2018. Cultivar Fuji Zehn Aztec was the last cultivar harvested in 2017 on October 2 and in 2018 on October 5. The maximum number of seeds per fruit was noticed in the cultivar Mollie's Delicious (8.34 and 8.71) during both 2017 and 2018, respectively. Cultivar Starkrimson had the fewest seeds per fruit in 2017 (7.11) and 2018 (7.42). Cultivar Baleman's Cider had the highest acidity in 2017 (0.63%) and 2018 (0.52%). In both 2017 (0.25%) and 2018 (0.23%), the Adam's Pearmain cultivar was the least acidic. Cultivar Allington Pippin (16.13 °Brix) and Red Gold (16.73 °Brix) had the highest TSS in 2017 and 2018, respectively, whereas Vance Delicious (12.30 °Brix) and Top Red (10.78 °Brix) had the lowest TSS in 2017 and 2018, respectively. The cultivars Mollie's Delicious and Red Gold had the highest total sugars (11.33 and 11.40%) in 2017 and 2018, respectively. Cultivar Baleman's Cider had the lowest total sugars (9.82%) in 2017 while Top Red (9.78%) in 2018. The cultivar Vance Delicious had the highest ratio of leaves to fruits in 2017 (55.44) and for Shalimar Apple-2 in 2018 (49.65). In 2017, cultivars Fuji Zehn Aztec (29.26) and Silver Spur (24.51), had the fewest leaves per fruit. The highest leaf chlorophyll content was recorded in cultivar Shireen (3.50 and 3.57 mg g−1 fresh weight) during the years 2017 and 2018, respectively. Cultivar Baleman's Cider had the lowest leaf chlorophyll content (2.15 mg g−1 fresh weight) during 2017, while cultivar Allington Pippin (2.09 mg g−1 fresh weight) had the lowest leaf chlorophyll content in 2018. The cultivars Fuji Zehn Aztec, with a yield efficiency of 0.78 kg/cm2 and Silver Spur with a yield efficiency of 1.14 kg/cm2 were the most yield efficient during the years 2017 and 2018, respectively. Cultivar Shalimar Apple-2 was least performing with yield efficiencies of 0.05 and 0.07 kg/cm2 during 2017 and 2018, respectively.The findings suggest that cultivar Mollie's Delicious commercially matures first and has the highest fruit length, diameter, and weight; hence, it can be a good option for cultivation so as to fetch the maximum price in the market when other cultivars are still maturing. Shalimar Apple-2 is precluded for cultivation due to least yield efficiency, whereas cultivars Fuji Zehn Aztec and Silver Spur are recommended to farmers for their higher yield efficiency.

Technological advances have played a critical role in the production of flower crops, enabling farmers to maximize yields and reduce losses while also improving the quality of flowers. These advances have included the development of new breeding techniques, such as molecular marker-assisted breeding, and the use of modern technologies like high-throughput phenotyping to identify and select superior cultivars. In addition, precision farming techniques, such as the use of sensors and remote monitoring systems, have made it possible to closely monitor crop growth and optimize inputs like water and fertilizer, leading to higher yields and improved resource efficiency. Advancements in biotechnology have also resulted in the development of transgenic plants that are resistant to pests and diseases, reducing the need for chemical pesticides and improving plant health. Modern molecular genetic tools, particularly genome editing with CRISPR/Cas9 nucleases, are emerging in addition to conventional approaches of investigating these plants. Furthermore, the use of novel growing systems, such as hydroponics and vertical farming, has allowed for year-round flower production in controlled environments, mitigating the challenges associated with seasonal changes and climate variability. These innovations have also made it possible to produce high-quality flowers in urban areas, bringing fresh blooms closer to consumers. Overall, technological advances in flower crops have revolutionized the floriculture industry, enabling growers to produce high-quality flowers in a more sustainable and efficient manner. These advancements have not only improved the productivity and profitability of flower farming but have also contributed to the conservation of natural resources and the protection of the environment.

Current study was conducted to evaluate the effect of important land uses and soil depth on soil organic carbon pools viz. total organic carbon, Walkley and black carbon, labile organic carbon, particulate organic carbon, microbial biomass carbon and carbon management index (CMI) in the north Western Himalayas, India. Soil samples from five different land uses viz. forest, pasture, apple, saffron and paddy-oilseed were collected up to a depth of 1 m (0–30, 30–60, 60–90 cm). The results revealed that regardless of soil depth, all the carbon pools differed significantly (p

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump’s inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time–frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.

This paper proposes a new method for bearing fault diagnosis using wavelet packet transform (WPT)-based signal representation and informative factor linear discriminant analysis (IF-LDA). Time–frequency domain approaches for analyzing bearing vibration signals have gained wide acceptance due to their effectiveness in extracting information related to bearing health. WPT is a prominent method in this category, offering a balanced approach between short-time Fourier transform and empirical mode decomposition. However, the existing methods for bearing fault diagnosis often overlook the limitations of WPT regarding its dependence on the mother wavelet parameters for feature extraction. This work addresses this issue by introducing a novel signal representation method that employs WPT with a new rule for selecting the mother wavelet based on the power spectrum energy-to-entropy ratio of the reconstructed coefficients and a combination of the nodes from different WPT trees. Furthermore, an IF-LDA feature preprocessing technique is proposed, resulting in a highly sensitive set of features for bearing condition assessment. The k-nearest neighbors algorithm is employed as the classifier, and the proposed method is evaluated using datasets from Paderborn and Case Western Reserve universities. The performance of the proposed method demonstrates its effectiveness in bearing fault diagnosis, surpassing existing techniques in terms of fault identification and diagnosis performance.

The aim of the study was the evaluation of silicon foliar application on sugar beet yield. In the years 2017–2019, the effect of a single foliar application of various forms of silicon (potassium silicate—PS, calcium silicate—CS, sodium metasilicate—SM, and orthosilicic stabilized acid—OSA) applied in the six-leaf phase of sugar beet, 7 and 14 days later on yield and technological quality of sugar beet roots was assessed. It was found that the form of silicon does not have a significant effect on the yield of sugar beet roots, and significantly modifies the biological yield of sugar and the pure sugar yield. The highest biological yield of sugar is achieved by the foliar application of PS, and the pure sugar yield by PS and OSA. The date of foliar application as well as the interaction of the date of application and silicon forms do not have a significant effect on the root yield, biological yield of sugar, and pure sugar yield. The form of silicon has a significant effect on the technological quality of sugar beet roots (sugar, α-amino nitrogen, potassium, and sodium content). The most beneficial effect on the sugar content and reduction of sodium content in sugar beet roots is the foliar application of OSA, and the reduction of α-amino nitrogen and potassium content—PS. The timing of the application of various forms of silicon has a significant effect on the sugar and potassium content in sugar beet roots. The most beneficial effect on the sugar content in the roots is the application carried out 7 days after the six-leaf phase of sugar beet, and the potassium content is most limited by the treatment 14 days after reaching this phase. The interaction of the timing of foliar application and the form of silicon significantly modifies the technological quality features of sugar beet roots: the content of sugar, α-amino nitrogen, potassium, and sodium. The results of the study proved the significant effect of silicon foliar application on the physiological parameters of plants, such as leaf area index (LAI), absorption of photosynthetically active radiation (PAR) and normalized difference vegetation index (NDVI) which are related to yield and sugar beet productivity.

The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R2), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R2 ~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R2 ~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R2 ~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R2 of 0.99. The lowest R2 and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R2 as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices.

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

In this paper, an approach to perform leak state detection and size identification for industrial fluid pipelines with an acoustic emission (AE) activity intensity index curve (AIIC), using b-value and a random forest (RF), is proposed. Initially, the b-value was calculated from pre-processed AE data, which was then utilized to construct AIICs. The AIIC presents a robust description of AE intensity, especially for detecting the leaking state, even with the complication of the multi-source problem of AE events (AEEs), in which there are other sources, rather than just leaking, contributing to the AE activity. In addition, it shows the capability to not just discriminate between normal and leaking states, but also to distinguish different leak sizes. To calculate the probability of a state change from normal condition to leakage, a changepoint detection method, using a Bayesian ensemble, was utilized. After the leak is detected, size identification is performed by feeding the AIIC to the RF. The experimental results were compared with two cutting-edge methods under different scenarios with various pressure levels and leak sizes, and the proposed method outperformed both the earlier algorithms in terms of accuracy.

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann–Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann–Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann–Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann–Whitney test form the new fault-specific Mann–Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline’s acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time–frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

Mandibular angle fractures (MAFs) are treated in a variety of ways; however, the standard therapy is still up for debate. Despite the fact that many studies have generated evidence for the appropriate biomechanical stability of 3-D miniplates, there is an insufficient amount of data on the treatment of mandibular angle fractures with these plates. A comparative study was conducted at The Department of Oral and Maxillofacial Surgery, Pakistan Institute of Medical Sciences (PIMS), Islamabad. Patients were randomly divided into two groups of 52 patients each. Patients in group A were treated with 3-D miniplate placement on the lateral cortex following the principle of 3-D fixation proposed by Farmand and Dupoirieux, whereas patients included in group B were treated using 2-D conventional miniplate, placed according to Champy’s line of ideal osteosynthesis. A single surgical team performed the procedure. On the first and seventh post-operative days, the first month, and then the third month after surgery, regular evaluations were conducted. Assessment regarding Post Open Reduction and Internal Fixation (ORIF) occlusion was performed with the help of measuring tools. On the first day post-operative follow-up, 41 (78.8%) patients in group A and 31 (59.6%) patients in group B had satisfactory occlusion. The seventh day post-operative follow-up showed that 43 (82.7%) patients in group A and 41 (78.8%) patients in group B had satisfactory occlusion (p > 0.05). In both treatment groups, the first and third month follow-up evaluations revealed optimal occlusion. In comparison to conventional 2-D miniplate, the 3-D miniplate system produced better results and can be recommended as a better option for the management of mandibular angle fractures.