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This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability.

Background: Left-sided mechanical prosthetic heart valve thrombosis (PVT) occurs because of suboptimal anticoagulation and is common in low-resource settings. Urgent surgery and fibrinolytic therapy (FT) are the two treatment options available for this condition. Urgent surgery is a high-risk procedure but results in successful restoration of valve function more often and is the treatment of choice in developed countries. In low-resource countries, FT is used as the default treatment strategy, though it is associated with lower success rates and a higher rate of bleeding and embolic complications. There are no randomized trials comparing the two modalities. Methods: We performed a single center randomized controlled trial comparing urgent surgery (valve replacement or thrombectomy) with FT (low-dose, slow infusion tissue plasminogen activator, tPA) in patients with symptomatic left-sided PVT. The primary outcome was the occurrence of a complete clinical response, defined as discharge from hospital with completely restored valve function, in the absence of stroke, major bleeding or non-CNS systemic embolism. Outcome assessment was done by investigators blinded to treatment allocation. The principal safety outcome was the occurrence of a composite of in-hospital death, non-fatal stroke, non-fatal major bleed or non-CNS systemic embolism. Outcomes will be assessed both in the intention-to-treat, and in the as-treated population. We will also report outcomes at one year of follow-up. The trial has completed recruitment. Conclusion: This is the first randomized trial to compare urgent surgery with FT for the treatment of left-sided PVT. The results will provide evidence to help clinicians make treatment choices for these patients.(Clinical trial registration: CTRI/2017/10/010159).

Powder characteristics such as flow energy, bulk density, compressibility, morphology, particle size distribution (PSD) and laser energy absorbtivity have a significant impact on the Laser Bed Powder Fusion (L-PBF) process. The impact of powder characteristics on in-situ nickel-titanium alloy formation within the L-PBF process is not well understood. In this work, the characteristics of nickel, titanium and two elemental blends of nickel-titanium powder were compared with those of a pre-alloyed nitinol powder. L-PBF solidification tracks were generated using selected powders and characterised for resulting track dimensions and chemical homogeneity. This study demonstrates that the melt homogenisation time and the size of titanium particles relative to nickel particles are critical factors for successful in-situ alloying of nitinol within the L-PBF process. The variation in elemental composition within solidification track cross sections, as measured by EDX point spectra, was found to increase significantly for laser scanning speeds of 400 mm/s and above. Furthermore, the elemental composition inhomogeneity with increasing laser scan speed was found to be significantly more pronounced for a nickel-titanium powder blend of spherical particles with average titanium particle size of 48 μm compared to an otherwise similar blend with an average titanium particle size of 32 μm. There was no significant difference recorded between solidification track widths of in-situ alloyed blends compared to pre-alloyed blends, indicating that optimal hatch spacing for in-situ alloying is likely to be similar to that required for pre-alloyed powders.

This study investigates the greater coefficient of Performance (COP) exhibited by the Ni–Ti lattice geometry (LS) over the solid geometry (SS) for solid-state heating and cooling. The LS's advantageous COP can be attributed to factors including variations in phase transformation behavior, a larger surface area facilitating enhanced heat transfer, and a greater volume of water heated per cycle owing to the cavities. Moreover, the LS demands lower compression forces due to its lower mass and geometry, which contribute to its heightened energy efficiency. Remarkably, the LS achieved a maximum temperature span of 11.2 °C at 40 kN compression force, whereas the SS required 80 kN to attain a similar 12.9 °C span. Both the SS and LS showcased a non-linear correlation with compression force which was attributed to Lauder's transformation and pseudo-elastic behaviors. Additionally, the temperature span of the SS exhibited symmetrical behavior during heating-cooling cycles. Conversely, the LS displayed asymmetry, with a more substantial temperature change during the cooling phase than the heating phase.

The proposed Sensor Data Encryption with Compression (SEC) system is presented as an innovative solution for sensor data processing, aiming to achieve optimal efficiency while improving security and adaptability by integrating robust cryptography with advanced compression techniques. The system's objective is to create a reliable framework for managing sensitive sensor data by effectively tackling challenges in data processing through a balanced combination of strong cryptography and sophisticated compression methods. The SEC system utilizes advanced encryption and compression techniques, including the scrambling of Burrows-Wheeler Transform (BWT), Move-to-Front (MTF) encoding, Run-Length Encoding (RLE), and Huffman coding, to attain exceptional compression efficiency while maintaining data integrity. The practical implementation of the SEC system shows an impressive 85 % enhancement in data compression efficiency, as evidenced by reduced Bits per Character (BPC) across various sensor data inputs, along with improved resistance to cryptanalysis due to an increased unicity distance. Consequently, the SEC system demonstrates itself to be a robust and efficient solution, achieving superior data compression and enhanced cryptographic resilience, thereby addressing critical challenges in sensor data processing.

The demand for affordable prostheses is particularly high in Low Middle-Income Countries (LMICs). Currently, sockets are predominantly manufactured using monolithic thermoplastic polymers, which lack durability and strength, or consumptive thermoset resin reinforcing with expensive composite fillers like carbon, glass, or Kevlar fibers. However, there exist unmet and demanding needs among amputees for procuring low-cost, high-strength, and faster socket manufacturing methods. We evaluate a socket made from a novel manufacturing technique utilizing an affordable and sustainable composite material called commingled PET (polyethylene terephthalate) yarn, along with a reusable vacuum bag, to produce custom-made sockets in a purpose-built curing oven. Our innovative fabrication methodology enables the production of complex-shaped patient sockets in under 4 h. To evaluate the efficacy and performance of the PET sockets, we conducted trials with both unilateral and bilateral amputees over a six-month period, in collaboration with Bhagwan Mahaveer Viklang Sahayata Samiti (BMVSS) in India. Utilizing a 6-min walking test, we measured various gait parameters, including ground reaction forces and flexion angle, for both unilateral and bilateral amputees.The gait analysis conducted on amputees using our PET-based sockets demonstrated their ability to engage in daily activities without interruptions, reaffirming the functional efficacy of our approach. By combining self-reinforced PET with our novel fabrication technique, we offer a unique and accessible solution that benefits clinicians and patients alike. This study represents significant progress towards achieving affordable and personalized prostheses that cater to the needs of LMICs.

Study region: The Shashe catchment, Limpopo River Basin, Botswana, and Zimbabwe. Study focus: The Shashe catchment is the third largest flow contributor to the Limpopo River Basin. Water availability in the Shashe catchment is highly seasonal due to high seasonal rainfall variability. The seasonality and inter-annual variability cause shortfalls (demand exceeds the average water availability) in certain months and years. Storage is needed to bridge the seasonal water availability “gap” and mitigate the deficits in drought years, i.e., inter-annual variability. While the need for water storage through grey infrastructure such as dams has long been known, there is growing recognition of the need for approaches to water storage that capitalize on all storage types. However, the current capacity to plan in ways that utilize all storage types is limited. The analyses conducted for this paper assessed the volume and spatial and temporal variability of different storage options – large and small dams, sand dams, soil moisture, and aquifers – in the Shashe catchment of the Limpopo River Basin. An integrated SWAT-MODFLOW model and remote sensing approach were developed for 2015–2020. New hydrological insights for the region: The total annual water storage in the Shashe catchment is approximately 44,000 Mm3, dominated by groundwater. The annual storage is about 42,000 Mm3 in aquifers, 1500 Mm3 in soil, 700 Mm3 in large dam reservoirs, 45 Mm3 in small dams/ponds, and 0.13 Mm3 in sand dams. There is high seasonality in water storage availability. Soil moisture storage is at its maximum from January to March and lowest from July to September. Dam storage is at its maximum from March to May, and the water storage is relatively stable throughout the year. Aquifer storage is relatively stable during the dry seasons compared to other storage options. Optimizing water use considering the seasonal variation in different storage types could improve water availability and climate resilience.

Although electrolytes prepared from natural substances have taken up research towards a greener future, practical difficulties in their mechanical and water resistive properties are yet to be addressed by many. In this current paper, a natural gum tragacanth is graft copolymerized with tertiary butyl acrylate utilising glutaraldehyde as the crosslinker and ascorbic acid and potassium persulfate as the initiators. From the TGA analysis, enhanced thermal stability is observed in the crosslinked membrane. The DSC analysis reveals that the crosslinked gum exhibits a glass transition temperature (Tg) of 66 °C and a melting temperature (Tm) of 86 °C. The water uptake of the crosslinked membrane was found to be lower than the pure non-crosslinked membrane. The stress-strain curves of the crosslinked electrolyte showed a tensile strength of 3.58Mpa and a percent elongation of 302%. The effect of the crosslinker glutaraldehyde and the ionic salt lithium nitrate is studied with the help of XRD and FTIR. The electrolyte is characterised to be a good ionic conductive membrane as its conductivity value is found to be 4.72 ×10−4 Scm−1 through EIS measurements. The Arrhenius behaviour of temperature-dependent conductivity was studied and the activation energy values for all the films were calculated.

Caenorhabditis elegans is widely used as a model to predict the virulence of animal-origin human pathogens, as C. elegans and humans share similar intestinal epithelial cells. A study was undertaken to assess the feasibility of C. elegans for surveillance of foodborne pathogens in fresh vegetables. First, the virulent strains of four foodborne pathogens viz., Pseudomonas aeruginosa (PAO1), Escherichia coli (O157), Salmonella enterica (CI1), and Staphylococcus aureus (ATCC25923) were fed to the worm as sole diet and compared the survival, phenome traits, and gut integrity with standard E. coli OP50. Secondly, the vegetable surface microbiota (tomato) was enriched with gradient concentrations of PAO1, and the worm's survival and gut distension and leakage were assessed. Thirdly, the total microbiota of fresh vegetables (tomato, brinjal, and lablab) were evaluated for the worm's survival. The gut epithelial cell damage and leakage were monitored by blue dye-based smurf assay. Foodborne pathogens reduced the median lifespan (LT50) of the worm drastically, with PAO1 had 2.5 days, O157 (6 days), CI1 (8 days), ATCC25923 (6 days), while the standard diet OP50 had 16 days. The phenome traits of worms, viz., pharyngeal pumping, defecation cycle, body bends, head thrashes, touch stimulus, paralysis, gut leakage, and reproduction, were significantly negatively affected due to pathogen feeding. The gut epithelial integrity was severely affected, and blue dye leakage was the highest in O157 (73 %), followed by PAO1 (55 %), CI1 (40 %) and ATCC25923 (38 %), while the gut epithelial cells were intact in OP50 fed worms. The pathogen enrichment in tomato surface microbiota diet showed that LT50 of the worms was reduced from 8 days to 2.5 days and 10 to 8 days with increasing gradient of PAO1 in fresh tomato microbiota and 24-hours pre-enriched tomato microbiota, respectively. Smurf test also confirmed that an increase in PAO1 proportion in total microbiota distended the gut with high dye leakage. The total microbiota extracted from tomato, brinjal, and lablab did not affect the worm's health and had an extended median life span (11 – 13 days) when they were devoid of any foodborne pathogens. These findings provide a novel and straightforward approach for assessing foodborne pathogens using animal models.

Monkeypox, now named mpox, has emerged as a significant public threat, as evidenced by the 2022 outbreak affecting over seventy countries globally. This infectious disease presents debilitating symptoms, including painful skin rashes, mucosal lesions, enlarged lymph nodes, and fever. The situation intensifies with concerns about a novel transmission route, specifically through sexual contact, and the evolution of more transmissible strains. Complicating matters further is the documented spillback of mpox from humans to animals, raising the potential for new animal reservoirs. This study utilized a systematic approach to gather, analyse, and interpret data regarding global mpox outbreaks, phylogenomics, human APOBEC3 enzyme activity, antiviral resistance issues, and the application of the One Health intervention. Emphasizing a One Health approach covers various aspects, including zoonotic origins, pathogenesis, changing epidemiological landscapes, phylogenomic diversity, and clade dynamics. The review underscores the crucial role of global collaboration in understanding and combatting mpox and making it a valuable resource for shaping effective prevention and control measures on a global scale.

Summary: Each cargo in a cell employs a unique set of motor proteins for its transport. To dissect the roles of each type of motor, we developed optogenetic inhibitors of endogenous kinesin-1, -2, -3 and dynein motors and examined their effect on the transport of early endosomes, late endosomes, and lysosomes. While kinesin-1, -3, and dynein transport vesicles at all stages of endocytosis, kinesin-2 primarily drives late endosomes and lysosomes. Transient optogenetic inhibition of kinesin-1 or dynein causes both early and late endosomes to move more processively by relieving competition with opposing motors. Kinesin-2 and -3 support long-range transport, and optogenetic inhibition reduces the distances that their cargoes move. These results suggest that the directionality of transport is controlled through regulating kinesin-1 and dynein activity. On vesicles transported by several kinesin and dynein motors, modulating the activity of a single type of motor on the cargo is sufficient to direct motility.

The ever-burgeoning sustainable need for humanity to produce lighter, tougher, and more cost-effective materials has led to the development of biodegradable composites. Ever since their creation, natural fiber-based composites have found themselves ubiquitous. Due to their exceptional performance, Natural fiber-reinforced composites have been predominantly used in several engineering applications. Coconut leaf sheath (CLS) is an abundantly available agro-waste that can be easily extracted from the coconut tree. This review investigates the potential of incorporating coconut sheath into polymeric matrices. Also, the effects of surface treatments, synthetic fiber hybridization, and nanofiller-modified matrices were analyzed in detail. It has been observed that surface modification of coconut sheath, hybridization with other natural or synthetic fibers, and nanofiller-modified polymeric composites exhibit better mechanical performance compared to monolithic coconut sheath-based polymeric composites. One of the key advantages of hybrid composites is that they can combine the strengths of different constituents to overcome their individual limitations. Moreover, coconut sheath-based hybrid composites enhance the composites' damage tolerance and reduce the material cost.

Ovarian cancer continues to have a high mortality rate despite therapeutic advances. Traditionally, treatment has focused on surgery followed by systemic platinum- based chemotherapy. Unfortunately, most patients develop resistance to platinum agents, highlighting the need for targeted therapies. PARP inhibitors and anti-angiogenic agents, such as bevacizumab, have more recently changed upfront therapy. Unfortunately, other targeted therapies including immunotherapy have not seen the same success. Emerging therapeutic targets and modalities such as small molecule tyrosine kinase inhibitors, lipid metabolism targeting agents, gene therapy, ribosome targeted drugs as well as several other therapeutic classes have been and are currently under investigation. In this review, we discuss targeted therapies in high grade serous ovarian cancer from preclinical studies to phase III clinical trials.

This communication describes a highly recyclable, ecologically friendly, ionic liquid catalyst supported by cobalt that is active in the Suzuki-Miyaura reaction at 70 ​°C. To be more precise, the procedure is initiated with a special form of the ecologically safe solvent 3-(2-aminoethyl)-1-methyl-1H-imidazole-3-ium bromide [Aemim]Br. A few benefits of the coupling process include its simplicity of use, broad range of substrate compatibility with high yields, environmental friendliness, high reaction rate, low catalyst loading (0.001 ​mol%), and recyclability (up to 7 cycles).

Elastocaloric Ni-Ti alloys offer solid-state heat pump and cooling applications. The environmental impact of elastocaloric Ni-Ti parts manufactured via powder bed fusion was assessed using life cycle assessment with the ReCiPe method. These solid-state heat pumps can mitigate the environmental impacts of current air conditioning/refrigeration methods that use harmful chemicals like CFCs, HCFCs, and HFCs. Measurements of electricity, Ar gas, and compressed air consumption were considered within the gate-to-gate boundary, covering powder transportation, printing, and use-phase. Nickel was identified as the most damaging input, reducible through geometry optimization. Optimized geometries (given the same number of printing layers) require more energy during powder bed fusion however, the environmental benefit from material saving outweighs the damage from increased electrical usage by far. The powder bed fusion machine's standby mode consumes more energy and Ar gas than the printing process, presenting an opportunity for mitigation. Additively manufactured Ni-Ti elastocaloric structures were used as heat generators/sinks in flowing water during compression cycles, achieving 11 °C and 10 °C water temperature change per cycle for the solid geometry and the optimized geometry respectively. A 10 x 10 × 55 mm Ni-Ti structure, providing >2 °C temperature span under compressive cycles, served as the functional unit.

Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in early diagnosis and treatment. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool in the field of medical image analysis, revolutionizing the way cancer is detected and diagnosed. The study discusses the various modalities employed in lung cancer diagnosis, such as medical imaging (e.g., radiology and pathology), genomics, and clinical data, highlighting the unique challenges associated with each domain. The proposed Multimodal Fusion Deep Neural Network (MFDNN) architecture design effectively integrates information from different modalities (e.g., medical imaging, genomics, clinical data) to enhance lung cancer diagnostic accuracy. Furthermore, it delves into the integration of clinical data, electronic health records, and multimodal approaches to improve the accuracy and reliability of lung cancer diagnosis. Moreover, we highlight the ethical considerations surrounding the deployment of Artificial Intelligence (AI) in clinical settings and the need for robust validation and regulatory guidelines. The Multimodal Fusion Deep Neural Network (MFDNN) achieves an exceptional accuracy rate of 92.5 %, marking a significant breakthrough in the realm of medical AI. MFDNN excels in precision, with 87.4 % accuracy in predicting cancer cases, and equally impresses in recall, capturing approximately 86.4 % of actual cancerous cases. The F1-score of 86.2 further exemplifies MFDNN's ability to strike a harmonious equilibrium, ensuring both diagnostic accuracy and minimized missed diagnoses. The performance is compared with established methods like CNN, DNN, and ResNet. The results underscore MFDNN's pivotal role in revolutionizing lung cancer diagnosis, promising more accurate and timely identification of this critical condition.

This study investigates the synthesis of cobalt and nickel ferrite NPs using Araucaria heterophylla resin as a bio- template and nitrate salts of cobalt, nickel, and iron as precursors.The resulting spinel systems exhibit efficient degradation of organic dyes, including methylene blue and rhodamine-B, and demonstrate excellent reusability. The NPs antibacterial efficacy against pathogenic bacteria, such as Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Salmonella paratyphi, was also evaluated. Comprehensive characterization of the nanomaterials was performed using FT-IR, XRD, SEM, TEM, EDX, and VSM techniques. Magnetic measurements revealed the ferromagnetic nature of cobalt ferrites and the superparamagnetic nature of nickel ferrites, with reduced saturation magnetization and coercivity compared to bulk materials due to the quantum size effect. The nano-formulated ferrites exhibited superior antibacterial activity, highlighting their potential for water treatment applications. This study underscores the importance of removing dye contaminants from wastewater before discharge into aquatic ecosystems.

The bromodomain and extraterminal (BET) family of proteins show altered expression across various cancers. The members of the bromodomain (BRD) family contain epigenetic reader domains that bind to acetylated lysine residues in both histone and non-histone proteins. Since BRD proteins are involved in cancer initiation and progression, therapeutic targeting of these proteins has recently been an area of interest. In experimental settings, JQ1, a commonly used BRD inhibitor, is the first known inhibitor to target BRD-containing protein 4 (BRD4), a ubiquitously expressed BRD and extraterminal family protein. BRD4 is necessary for a normal cell cycle, and its aberrant expression activates pro-inflammatory cytokines, leading to tumor initiation and progression. Various BRD4 inhibitors have been developed recently and tested in preclinical settings and are now in clinical trials. However, as with many targeted therapies, BRD inhibitor treatment can lead to resistance to treatment. Here, we investigated the kinases up-regulated on JQ1 treatment that may serve as target for combination therapy along with BRD inhibitors. To identify kinase targets, we performed a comparative analysis of gene expression data using RNA from BRD inhibitor-treated cells or BRD-modulated cells and identified overexpression of several kinases, including FYN, NEK9, and ADCK5. We further validated, by immunoblotting, the overexpression of FYN tyrosine kinase; NEK9 serine/threonine kinase and ADCK5, an atypical kinase, to confirm their overexpression after BRD inhibitor treatment. Importantly, our studies show that targeting FYN or NEK9 along with BRD inhibitor effectively reduces proliferation of cancer cells. Therefore, our research emphasizes a potential approach of utilizing inhibitors targeting some of the overexpressed kinases in conjunction with BRD inhibitors to enhance therapeutic effectiveness.

Dynamic changes in the endoplasmic reticulum (ER) morphology are central to maintaining cellular homeostasis. Microtubules (MT) facilitate the continuous remodeling of the ER network into sheets and tubules by coordinating with many ER-shaping protein complexes, although how this process is controlled by extracellular signals remains unknown. Here we report that TAK1, a kinase responsive to various growth factors and cytokines including TGF-β and TNF-α, triggers ER tubulation by activating αTAT1, an MT-acetylating enzyme that enhances ER-sliding. We show that this TAK1/αTAT1-dependent ER remodeling promotes cell survival by actively downregulating BOK, an ER membrane-associated proapoptotic effector. While BOK is normally protected from degradation when complexed with IP3R, it is rapidly degraded upon their dissociation during the ER sheets-to-tubules conversion. These findings demonstrate a distinct mechanism of ligand-induced ER remodeling and suggest that the TAK1/αTAT1 pathway may be a key target in ER stress and dysfunction.

The present work presents the effective application of waste heat rejected from window air conditioning (AC) in a waste heat dryer (WHD) for drying plantain banana slices. The comparison between drying with the waste heat dryer coupled with an AC unit (ACWHD) and a natural convective indirect solar dryer (SD) reveals significant differences in drying kinetics, supply air conditions, and thermodynamic performance. The drying kinetics of banana slices obtained with ACWHD and SD were validated by revisiting the concepts of dryer performance index (DPI) and generalized drying curve. The temperature of the hot air supplied to the drying chamber in ACWHD was 48 °C while the relative humidity of the supply air was below 30 %. The thermodynamic performance assessment revealed that the exergetic efficiency was about 45 % and 16 % for ACWHD and SD respectively. The results of exergy based sustainable index and environment impact factor indicated the potential application of the waste heat of residual air from AC units for drying applications. Thus, the results of the present study imply that this novel idea has appreciable potential for commercialization with large-scale central air conditioning units in commercial complexes and industries where the magnitude of heat rejection will be multi-fold.

This work examines the mechanical properties and structural aspects of Al-Hybrid fibre metal laminates (HFMLs) reinforced with fibres from basalt, hemp, and bamboo, including commercial-grade Al6061 alloy mesh (Al-DMEM). Compression moulding of sustainable fibres into various stacking configurations, such as BAlB, BHAlHB, and BBbAlBbB, was required for fabrication. Using cutting-edge methods like SEM and EDX for microstructure investigation, extensive testing was carried out, including tensile, flexural, impact, interlaminar shear, hardness, and water absorption tests. Basalt/Bamboo/Aluminum/Basalt (BBbAlBbB) was the FML configuration with the best mechanical parameters, showing Vickers hardness (24.23 MPa), tensile strength (44.537 MPa), flexural strength (218.24 MPa), and impact resistance (2.01 J) (59.982 HV). In addition, BAlB showed 83.9 % Vickers hardness, 41.7 % flexural strength, and 90.4 % tensile strength of BBbAlBbB samples. BHAlHB displayed 92.9 % of the flexural strength, 65.3 % of the tensile strength, and 93.7 % of the Vickers hardness of BBbAlBbB. Bamboo is hydrophobic by nature, so adding bamboo fibres to BBbAlBbB significantly boosted its resistance to moisture. This study highlights the potential of hybrid FMLs as high-performance, eco friendly materials that are particularly well-suited for applications in interior and exterior panels for micro-mobility (E-vehicles), with AL-DMEM reinforcement offering significant benefits like improved fibre bonding and structural integrity.

Extensive research efforts have been devoted to using greenhouse gas CO2 in upgrading bio-derived feedstock to value-added chemicals using the oxidative dehydrogenation route (CO2-ODH) with low-energy input. To realize the effective deployment of CO2-ODH at an industrial scale, it is imperative to advance the development of robust catalysts that can selectively catalyze C-H over C-C bonds, while simultaneously demonstrating thermodynamic stability to coke formation or sintering. Transition metal-based catalysts exhibit significant potential for being highly selective and reactive in the simultaneous conversion of CO2 and hydrocarbons, owing to their surface reducibility, well-balanced acid/base properties, and dynamic oxygen storage capacity. To stimulate further optimization, it's crucial to discover key design principles, potential trends and descriptors, and effective strategies to fine-tune the catalyst materials. This review comprehensively examines experimental and theoretical research aimed at pinpointing the key catalytic characteristics that affect selectivity in transition metal-based mono, bi, and multimetal oxides. It covers aspects like combinations of active metals and supports, effects of composition and alloying, interfacial structures, adsorption strengths, dynamics of in-situ restructuring, defect creation, surface morphology, and electronic properties, among others. We wrap up by suggesting approaches to overcome present obstacles in catalyst design and reactor technology, potentially bridging the lab-to-industry gap in this domain.

In the context of 5G wireless networks, this paper addresses a crucial concern—data loss in Analog-to-Digital Converters (ADCs) embedded within Multiple Input Multiple Output (MIMO) channels, a prevalent challenge in modern wireless communication systems. Despite the integration of ADCs in MIMO configurations to mitigate this issue, their implementation introduces significant complexities. In response to this, the paper introduces an innovative and efficient approach aimed at enhancing channel estimation in 5G MIMO systems. The proposed methodology relies on a heuristic-based optimization technique within a channel prediction framework. The framework uses feedback information collected through a Cascade Fusion Serial Network (CFSN) to analyze the receiver-side error ratio and estimate the channel coefficients of MIMO systems at the transmitter. An attention mechanism is incorporated into the model design through the use of a Convolutional Neural Networks - Gated Recurrent Units (CNN-GRU) and a Multi-Scaled Stacked Auto encoder. Parameters of Attention CNN-GRU, Stacked Auto encoder, and CFSN are refined using the Whale Optimization method. The optimization objective is to minimize key metrics, including Root Mean Squared Error (RMSE), Bit Error Rate (BER), and Mean Squared Error (MSE) associated with the estimated channel. Experimental evaluations underscore the efficacy of the proposed MIMO model in the context of 5G networks. The results demonstrate improved convergence rates, heightened prediction performance, and reduced computational costs compared to existing methods. This research contributes valuable insights into addressing data loss challenges specific to 5G MIMO systems, providing a pioneering solution that integrates heuristic optimization, advanced neural network architectures, and nature-inspired algorithms for parameter tuning.

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including pre-processing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrast-limited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inception-v3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%.

In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes • An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections. • A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset. • Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell.While the model is evaluated on Lung Cancer data, the architecture offered in the proposed solution is cancer agnostic. It may be scaled out to other cancer cell data if the properties are similar. The work presents a viable route for customizing treatments and improving patient outcomes in oncology by combining XAI with a large dataset. This research attempts to create a framework where a user can upload a test case and receive forecasts with explanations, all in a portable PDF report.

Background: The COVID-19 pandemic disproportionately impacted Latin America (LATAM), significantly disrupting cardiovascular testing. This study evaluated cardiac procedure recovery in LATAM one year after the outbreak. Methods: The International Atomic Energy Agency (IAEA) surveyed 669 centers in 107 countries worldwide, including 135 facilities in 19 LATAM countries, to assess cardiovascular procedure volumes in March 2019, April 2020, and April 2021, and changes in center practices and staffing conditions one year into the COVID-19 pandemic. Findings: LATAM centers reported a 21 % decrease in procedure volumes in April 2021 from pre-pandemic-baseline, vs. a 0 % change in the rest of the world (RoW), and greater volume reductions for almost all procedure types. Centers in Central America and Mexico reported the largest procedure reductions (47 % reduction) compared to the Caribbean (15 %), and South America (14 %, p = 0.01), and this LATAM region was a significant predictor of lower procedure recovery in multivariable regression. More LATAM centers reported reduced salaries and increased layoffs of clinical staff compared to RoW, and LATAM respondents estimated that half of physician and non-physician staff experienced excess psychological stress related to the pandemic, compared to 25 % and 30 % in RoW (p

The present work focused on molecular structure and various spectroscopic studies of 2-[2,3-dichloro-4-(2-methylidenebutanoyl)phenoxy] acetic acid is also called as Ethacrynic acid (EA). The B3LYP/6-311G(d,p) level of theory was adopted to compute the molecular geometry (monomer and dimer) for EA molecule, the computed geometrical parameters are well coincided with related XRD data. The Natural Bond Orbital (NBO) was calculated to find donor-acceptor interaction. The vibrational modes of EA molecule was computed by B3LYP/6-311G(d,p) method and assignments were carried out by finding Potential Energy Distribution (PED) using VEDA software. The deviation in wavenumber of O-H vibrations (in COOH) group evident the intermolecular hydrogen bond interaction is possible between nearby molecules. The Gauge Independent Atomic Orbital (GIAO) method is used to find 1H and 13C NMR chemical shift and correlated with recorded NMR spectrum. The Time-Dependent density functional theory (TD-DFT) is used to determine the various electronic parameters. The intra, inter molecular and non covalent interactions of the EA molecule are examined by Atoms in Molecules (AIM), ELF, LOL and RDG analysis. From AIM analysis the EA molecule form C-H…...Cl hydrogen bond of energies 1.2863 and 1.1922 Kcal/mol in both monomer and dimer were determined. Mulliken atomic charge analysis is used find the electronegativity equalization and charge transfer in the molecular system. Fukui function carried out to identify electrophilic and nucleophilic attack in the EA molecule. Thermal (DTA) and thermogravimetric (TG) analyzes adopted to determine the thermal behavior of compound which denote the compound dissociate around 122.5 °C. The thermodynamic parameters for different temperature were also calculated by B3LYP/6-311G(d,p) method. The EA compound was monitor to the antibacterial activity by agar well diffusion method. Molecular docking analysis carried out using Autodock software with protein inhibit thyroid and breast cancer show binding energies of -4.79 and -3.99 Kcal/mol.

The present study deals with a comprehensive characterization of Sugarcane bagasse ash (SCBA) and its effect on pozzolanic reactivity. Utilizing SCBA as pozzolanic material reduces the ash disposal problem and enhance the properties of cementitious composites. The ash is pre-treated by two methods: sieving followed by incineration; and incineration followed by ball mill grinding inorder to increase the fineness or specific surface area, reduce loss on ignition and increase amorphous reactivity of sugarcane bagasse ash. The first method is energy intensive method, as the duration of sieving is only 15 min and incinerated at lowest temperature of 400 °C. The second method is a selective pre-treatment method, where incineration is done first to reduce LOI, followed by grinding for 60,120 and 180 min. The effect of processing methodology is studied by considering energy consumption, enhanced particle morphology of SCBA and pozzolanic reactivity of SCBA. The physio-chemical characterization is done by standard American, Indian codes. Enhanced micro-structural characterization is done by Lazer particle size analyzer, Scanning Electron Microscopy (SEM) test associated with Electro Dispersion Spectroscopy (EDS), X-ray diffraction (XRD) test, Thermo-gravimetric (TGA) studies. Further, the treated ash is tested for its pozzolanic reactivity by strength activity index and Frattini test. It is observed that sieving has consumed less energy of 11.5 kWh/t compared to grinding. However, the samples sieved couldn't perform well in pozzolanic reactivity due to the inert silica present in it. The particle morphology is enhanced compared to raw SCBA. SCBA samples incinerated and grinded have shown a maximum strength activity index of 82.99 % and 92.6 % with an energy consumption of 30.4 kWh/t and 45.6 kWh/t for grinding. The Frattini test results are in co-relation with strength activity index with a factor of 80.52.

It has been ascertained that trifluoroacetic acid accelerates the Knoevenagel condensation reaction of 5-bromosalicylaldehyde and ethyl ester of cyanoacetic acid. As a result, the 6-bromo-3-cyanocoumarin compound has been synthesized and the structure was eventually confirmed by X-ray analysis. Based on the structure-activity relationship and literature survey we found this compound has a potent inhibitor for MetAP (methionine aminopeptidase) and further we have studied molecular docking to understand the binding interactions and also performed ADME/T studies for this compound. The surface reactivity and intermolecular interactions of the studied compound were investigated by Hirshfeld surface analysis. Energy framework analysis shows the interaction between pair of fragments with neighbor molecules. Furthermore, the DFT study is performed to investigate structural reactivity and stability at B3LY/6-311+G(d,p) functional. The decreased HOMO-LUMO gap (3.97 eV) indicates its higher reactivity and soft nature. Molecular electrostatic potential (MESP) analysis and NBO charges indicate the structure's reactive site and charge transfer. Excited analysis by using the TD-DFT was performed to get excitation energy (ΔE), absorbance, and oscillator strength (fo) of crucial transition. Theoretically predicted values of polarizability (αo) and hyperpolarizability (βo) suggest their excellent optical and Nnar (NLO) properties.

We first report a novel solvent, base free synthesis of carbonyl compound catalyst by FeCl3/TEMPO at 40 °C with high-rate. In this article, we examine the mechanism of this catalytic system and compare the reactivity of aromatic alcohols. Furthermore, the efficacy of 3-(2-aminoethyl)-1-methyl-1H-imidazol-3-ium bromide [Aemim]Br could be maintained after seven recycling cycles. High catalyst productivity and reaction rate (5.53 to 22.50) have been accomplished with the use of an enhanced procedure.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

This paper compares the ENDF/B-VIII.0 library for the MOX-fueled VVER-1000 pin cell benchmark with ENDF/B-VII.0 library using the ν-TRAC code and demonstrates the modeling capability of the code. ν-TRAC is a MOC-based code for simulating neutron transport in fuel assembly lattices, with the ability to model cell heterogeneities. The material cross-section significantly impacts neutron transport calculations; therefore, a 172 multigroup cross-section library has been generated using the most recent version of ENDF/B-VIII.0 and the open-source NJOY-2016 code. This nuclear data file includes modifications to the cross-section of major actinides and other light nuclides. Five MOX fuel variants differing in fuel isotopic composition and five states differing in fuel temperature, the moderator, and poison in the moderator are investigated using the most recent library. The impact of new release on the infinite multiplication factor and reactivity change due to the different operating states is studied. Results of ν-TRAC code using multigroup data libraries based on ENDF/B-VIII.0 and ENDF/B-VII.0 are compared with corresponding results from SCALE/SAS2H, HELIOS, and MCU.

الملخص: تتناول هذه المراجعة الأحداث الجانبية السلبية المرتبطة بالجهاز المناعي والأمراض المناعية الذاتية (التي تعرف بالأحداث الجانبية السلبية بعد التطعيم) المرتبطة بلقاحات كوفيد-19، وتسلط الضوء على تكرارها، والحالات المبلغ عنها، والصلة مع فئات اللقاحات المحددة. مفهوم ''قلة الصفيحات الجلطية المناعية المستحثة باللقاح'' مهم في التعامل مع التشكك في اللقاحات. فهم هذه المفاهيم يساعد العاملين في الرعاية الصحية على تحديد وإدارة الأحداث الجانبية المحتملة بعد التطعيم. على الرغم من ندرتها، يمكن أن تسبب الأحداث الجانبية السلبية بعد التطعيم قلقا وتوترا بين المجتمع. يجب على العاملين في الرعاية الصحية والوكالات الصحية العامة مراقبة هذه الأحداث بنشاط والتعامل معها للحفاظ على ثقة المجتمع في برامج التطعيم. يتضمن ذلك الكشف السريع عن الحوادث المشبوهة، وتبني إجراءات للتخفيف من الأخطار، وإبلاغ المجتمع بمعلومات دقيقة وشفافة. البحث المستمر والمراقبة أمران ضروريان لفهم الآليات الممكنة وراء هذه الأحداث الجانبية وتطوير استراتيجيات لتقليل حدوثها. Abstract: This review examines the immunological and autoimmune adverse events associated with COVID-19 vaccines, highlighting their frequencies, reported cases, and associations with specific vaccine classes. The concept of vaccine-induced immune thrombotic thrombocytopenia is crucial in addressing vaccine skepticism. Understanding this concept helps healthcare professionals identify and manage potential adverse events after vaccination. Despite their rarity, immunological and autoimmune adverse events cause concern and anxiety among the public. To maintain public trust in vaccination programs, healthcare professionals and public health agencies must actively monitor and address these adverse events, promptly disclose suspicious incidents, take measures to mitigate dangers, and inform the public with transparency and accurate information. Continuing research and surveillance are essential for understanding the underlying mechanisms of these adverse events and developing strategies to minimize their occurrence.

This study examined the effect of variations in the laser power and scan speed for 52.39 at.%Ni-47.61 at.%Ti samples produced via laser powder bed fusion (L-PBF). The laser power was varied between 150W and 180 W, the scanning speeds were varied between 500 mm/s and 1000 mm/s, and other process parameters were kept constant. The resulting microstructures and physical properties of the produced samples were evaluated. In particular, the phase type and phase transformation characteristics, composition, Vickers microhardness, and microstructures were examined. It was observed that variation in the mean microhardness and relative density were not significant at a constant scan speed of 1000 mm/s, regardless of the laser power value. Samples processed at a laser power of 180W (with all other parameters kept constant) exhibited a bimodal micro- and nano structures. It was found that the samples with bimodal microstructure had a higher lattice strain, extent of B2 phase, and higher phase transformation temperatures.

It is significant to investigate the transmission dynamics of vector-borne infection because it has a global impact, can help predict and prevent future outbreaks, is important for understanding the impact of climate change on public health, can lead to more effective control strategies, and can improve our comprehension of the dynamics of these infections. Our paper presents a new model for chikungunya virus infection, which considers treatment and vaccination, using the Atangana-Baleanu derivative within the framework of the Caputo definition. First of all, we examine the positivity and uniqueness of the solution for the model. Then, we find out the fundamental results of the suggested model, such as the steady-state without infection and the R0 value that indicates endemicity pertaining to the system. Furthermore, we verify the local asymptotic stability of the infection-free steady-state. Through the application of fixed-point theory, we demonstrate the existence of the solution and propose a numerical methodology to investigate the dynamic behavior of the model. Finally, we performed simulations to demonstrate the effects of vaccination, transmission probability, treatment, and index of memory on the system's solution pathways. The findings of this research suggest the most sensitive parameters of the system responsible for the control and prevention of the infection. Emphasizing the significance of the memory index as an attractive parameter, we propose its consideration to the policy makers as a control parameter for the prevention of the infection.

Natural fibers derived from renewable, environmentally sustainable sources are being considered for use as reinforcements in polymer composites. The current study focuses on the fabrication of composite materials using fiber extracted from the Cissus quadrangularis (CQ) natural plant as reinforcement, an epoxy polymer as a matrix, and calcium oxide (CaO) nanoparticles as filler material, with the weight ratio of Cissus quadrangularis fiber (CQF) and CaO filler materials are varied to quantify the biological and structural effects of CQF composite. X-ray diffraction (XRD) results demonstrate a 58% amorphous zone in this composite, while Fourier transforms infrared spectroscopy (FTIR) examination revealed the hydrophobic nature, chemical bonding, and cellulose content. Because of the increased amount of chopped CQF loading in this composite, the mechanical qualities result in an average of 23% better tensile strength. Scanning electron microscopic (SEM) analysis was used to analyze the failure analysis for fiber interaction with matrix, filler, and fiber pullouts under mechanical stress. The antibacterial research revealed that this CQF composite exhibits bacterial inhibitory activity suitable for biomedical applications.

The present research work describes the phyto-synthesis of Manganese dioxide nanoparticles (MnO2NPs) from the reduction of potassium permanganate using Martynia annua (M.annua) plant extract. From the literature review, we clearly understood the M.annua plant has anti-inflammatory activity. Manganese dioxides are important materials due to their wide range of applications. Their increased surface area gives them distinct capabilities, as it increases their mechanical, magnetic, optical, and catalytic qualities, allowing them to be used in more pharmaceutical applications. A detailed review of literature highlighting the issues related to this present work and its knowledge gap that none of the inflammatory activities had been done by MnO2 NPs synthesized from M.annua plant extract. So we selected this study. The product MnO2 NPs showed the wavelength centre at 370 nm and was monitored by UV–Vis spectra. The wave number around 600 cm−1 has to the occurrence of O–Mn–O bonds of pure MnO2 confirmed by FTIR spectroscopy. Transmission electron microscopy images showed the morphology of MnO2 NPs as spherical-shaped particles with average sizes at 7.5 nm. The selected area electron diffraction analysis exhibits the crystalline nature of MnO2 NPs. The obtained MnO2 NPs showed potential antioxidant and anti-inflammatory activity was compared to the plant extract. The synthesized MnO2 NPs have a large number of potential applications in the field of pharmaceutical industries. In the future, we isolate the phytocompounds present in the M.annua plant extract and conduct a study against corona virus. MnO2 produces manganese (III) oxide and oxygen, which increases fire hazard. But further research is required to understand their environmental behaviour and safety.

Drought stress severely affects the physiology and metabolism of plants. Fortunately, using plant growth-promoting bacteria can provide an environmentally friendly solution. Hence, the present experiment was conducted on the sorghum plant for drought amelioration by utilizing Bacillus sp. and Pseudacidovorax intermedius bacterization to the sorghum seeds under greenhouse conditions. Drought stress was induced by the withholding of water after 52 days of emergence (flowering stage). However, the inoculation of Bacillus sp. and Pseudacidovorax intermedius could significantly improve the drought tolerance capacity through the production of different antioxidant enzymes and metabolites in sorghum plants under drought and recovery conditions. The results declared that the inoculation of Bacillus sp. significantly reduced the membrane leakage by 37 % by minimizing the production of malondialdehyde (49 & 47 %) and hydrogen peroxide (32 & 36 %) content. In addition to protecting membrane leakage, bacterial inoculation positively modulates the antioxidant enzymes namely catalase, ascorbate peroxidase, glutathione peroxidase, superoxide dismutase, and phenol content in the plant system. Metabolomic results revealed that inoculation of Bacillus sp. and consortia of Bacillus sp. and Pseudacidovorax intermedius up regulate the carboxylic acid, organic, and amino acids biomolecules expression inside the inoculated plants. The study concluded that inoculation of Bacillus sp. is an assured technology for mitigating the drought effect through the expression of antioxidant compounds, and expression of leaf metabolites in sorghum during drought conditions. Further, the research needs to comprehend the underlying molecular mechanism of bacterial-induced drought-tolerant in sorghum plants. It will also create a road map to develop a bestselling bioinoculant for climate-smart agriculture.

Additive manufacturing (AM) technology offers a streamlined approach to producing intricate components, reducing both time and costs. Lattice structures play a crucial role in optimising design space by proposing lattices that evaluate a broad spectrum of effective thermal and mechanical properties for building applications. This study aims to assess the impact of lattice topology and variations in the number of unit cells on the K-value, U-value, and compression strength of additively manufactured (AM) polymer lattices. The polymer lattice patterns were created using commercially available 3D printers and PLA polymer filaments. Thermal properties were characterised using a commercial heat flow meter (HFM), and hot-box calorimetry was employed to determine the K-value and U-value, respectively. Additionally, a universal material testing machine was used to investigate the compression strength of all specimens. Based on experimental findings, a scaling law was employed to correlate the effective thermal conductivities of various polymer lattice topologies, estimating the U-value of each one. Furthermore, the study examined how AM process parameters influenced the U-value of polymer lattices. The results indicated that lattice topology and relative density (ρ‾) significantly affected the U-value. The study also demonstrated that polymer lattice structures can be designed to select the optimal lattice configuration. Varying lattice topology had a notable impact on compression strength. It can be concluded that triangular and diamond lattice specimens with ρ‾=40% outperformed other lattice topologies due to their superior mechanical properties, the flexibility of additive manufacturing, and faster manufacturing time. The design of diverse lattice topologies holds promise for incorporating porosity into rigid materials, achieving high thermal and mechanical performance for energy-saving applications.

Summary: Background: One-fifth of people with drug-resistance tuberculosis (DR-TB) who were initiated on newer shorter treatment regimen (with injection) had unfavourable treatment outcomes in India as on 2020. Evidence on self-driven solutions and resilience adapted by people with DR-TB (PwDR-TB) towards their multi-dimensional disease and treatment challenges are scarce globally, which we aimed to understand. Methods: In this qualitative study using positive deviance framework, we conducted semi-structured in-depth interviews among consenting adult PwDR-TB (7 women, 13 men) who completed shorter treatment regimen (including injections) with maximum treatment adherence. The study was conducted in the southern districts of Bengaluru and Hyderabad, India between June 2020 and December 2022. Caregivers (14 women, 6 men) and health providers (8 men, 2 women) of PwDR-TB were also interviewed. Interviews were conducted in local language (Kannada, Tamil, Telugu, Urdu and Hindi) and inquired about practices, behaviours, experiences, perceptions and attributes which enabled maximum adherence and resilience of PwDR-TB. Interviews were audio recorded, transcribed, and translated to English and coded for thematic analysis using inductive approach. Findings: Distinctive themes explanatory of the self-driven solutions and resilience exhibited by PwDR-TB and their caregivers were identified: (i) Self-adaptation towards the biological consequences of drugs, by personalised nutritional and adjuvant practices, which helped to improve drug ingestion and therapeutic effects. Also home remedies and self-plans for ameliorating injection pain. (ii) Perceptual adaptation towards drugs aversion and fatigue, by their mind diversion practices, routinisation and normalisation of drug intake process. and constant reinforcement and re-interpretation of bodily signs of disease recovery (iii) Family caregivers intense and participatory care for PwDR-TB, by aiding their essential life activities and ensuring survival, learning and fulfilling special nutritional needs and goal oriented actions to aid drug intake (iv) Health care providers care, marked by swift and timely risk mitigation of side-effects and crisis response (v) Acquired self-efficacy of PwDR-TB, by their decisive family concerns resulting in attitudinal change. Also being sensitised on the detrimental consequences of disease and being motivated through positive examples. Interpretation: Synthesised findings on self-driven solutions and resilience towards the multi–dimensional DR-TB challenges provides opportunity for developing and testing new interventions for its effectiveness in DR-TB care settings globally. Designing and testing personalised cognitive interventions for PwDR-TB: to inculcate attitudinal change and self-efficacy towards medication, developing cognitive reinforcements to address the perception burden of treatment, skill building and mainstreaming the role of family caregivers as therapeutic partners of PwDR-TB, curating self-adaptive behaviours and practices of PwDR-TB to normalise their drug consumptions experiences could be the way forward in building resilience towards DR-TB. Funding: United States Agency for International Development (USAID) through Karnataka Health Promotion Trust (KHPT), Bengaluru, India.

Summary: Sleep fragmentation (SF) disrupts normal biological rhythms and has major impacts on cardiovascular health; however, it has never been shown to be a risk factor involved in the transition from cardiac hypertrophy to heart failure (HF). We now demonstrate devastating effects of SF on hypertrophic cardiomyopathy (HCM). We generated a transgenic mouse model harboring a patient-specific myosin binding protein C3 (MYBPC3) variant displaying HCM, and measured the progression of pathophysiology in the presence and absence of SF. SF induces mitochondrial damage, sarcomere disarray, and apoptosis in HCM mice; these changes result in a transition of hypertrophy to an HF phenotype by chiefly targeting redox metabolic pathways. Our findings for the first time show that SF is a risk factor for HF transition and have important implications in clinical settings where HCM patients with sleep disorders have worse prognosis, and strategic intervention with regularized sleep patterns might help such patients.

Lead is an environmental contaminant that is considered a major public health concern due to its effects on various body systems, such as the neurological, cardiovascular, immune and renal systems. Scientific evidence indicates that health effects occur in people at blood lead levels (BLLs) lower than those previously established as levels of concern; no safe level of lead exposure has been established. Lead has been monitored as part of various national and international biomonitoring programs. The aims of this study were: (1) to examine BLLs in the Canadian general population and vulnerable sub-populations between 2007 and 2013 using data from the Canadian Health Measures Survey (CHMS); and (2) to assess factors that are associated with elevated BLLs in these populations. BLLs in Canadians were in a decreasing trend over the study period. BLLs in Canadians varied by age and sex. Those aged 60 to 79 years had the highest BLLs, while the 12 to 19 years age group had the lowest BLLs. Males exhibited significantly higher BLLs than females. In young children aged 3 to 5 years, 99.9 % had BLLs below 5 µg/dL (blood lead reference level for children). BLLs in pregnant women were lower than those in non-pregnant women in the same age range. Multivariable regression analysis suggested that factors such as immigration status, household income, educational level, age of dwelling, smoking status, second-hand smoke exposure, alcohol consumption, and fish and shellfish consumption, were significantly associated with BLLs. This study presents the first robust analysis of BLLs in the Canadian vulnerable populations and assessment of factors associated with elevated BLLs. This information may be useful to identify population subgroups who are differentially exposed to lead based on various exposure factors and are at risk for lead exposure in Canada.

Nowadays, the study of fractional differential equations (FDEs) is one of the most interesting research topics. In this article, we study a novel nonlinear fractional order Langevin system involving delays. The primary focus of this article is to analyze the relative controllability of a nonlinear integrodifferential fractional Langevin dynamical system with multiple delays in control for finite-dimensional spaces. Fundamentals of the fixed-point theory are used to achieve the desired results. Using Schauder’s fixed theorem, sufficient conditions for the controllability are established and the Mittag-Leffler matrix function defines the controllability Grammian matrix. Additionally, the derived results are validated through an application.

Superhydrophobic surfaces are currently a subject of great interest because of their tremendous applications. This study offers greater insight into the self-cleaning ability and anti-icing behavior of the superhydrophobic mild steel and finds application in aircraft and marine systems. Superhydrophobic surfaces were developed on the mild steel surface by improving the roughness and reducing the energy on the surface. Mild steel was chemically etched with HCL solution for 10 min to improve the roughness. After etching, the surfaces were immersed in stearic acid solution for 3 h by solution immersion method to introduce low surface energy. The resultant surfaces have a combination of nano and micro-roughness. The roughness of the samples was measured with the aid of surface roughness tester. The roughness of the prepared surface is 4.213 μm. The microstructure of the as-prepared surface was characterized by a scanning electron microscope. The surfaces have changed to be pine-cone-like structure. The pinecone-like hierarchical structures can generate numerous grooves in which the air can be trapped which lead to a larger contact angle. The contact angle was measured by using a goniometer setup. The obtained contact angle of the surface is as high as 154° with a lower contact angle hysteresis. The surfaces were tested for self-cleaning and ice-delaying properties. The as-prepared superhydrophobic surfaces can self-clean the surface from dirt/dust while the water droplets roll over the surface. A freezing delay time of 392 s was achieved on the surfaces. The prepared surfaces possess better self-cleaning and ice-delaying properties.

With the development of targeted therapies, many treatments are based on molecular studies, which require sampling tumor tissue from paraffin blocks for sequencing. An automated solution could potentially reduce the workload of pathologists by acting as a screening device and may reduce the subjectivity in diagnosis. In tissue-based diagnostics, most of the work still needs to be done manually by a pathologist using a microscope to examine stained slides. The foundation of such tasks is to accurately distinguish cancer/malignant cells from normal/benign cells. However, the determination of tumor content is poorly reproducible with significant variation. As the size of tumor regions can be very small, pathologists are often required to use high magnification for detecting tumor cells. This requirement significantly increases the workload for pathologists. As digital pathology datasets have become publicly available and have opened up the possibility of evaluating the feasibility of applying deep learning techniques to improving the efficiency and quality of histologic diagnosis. The model proposed in this work is an application to detect colorectal cancer based on the Convolutional Neural Network and Ranking algorithm. Based on the performance evaluation, it is found that the proposed model is yielding better results in diagnosis of Colorectal Cancer than the existing methods in terms of Recall, Precision and Accuracy.

This study presents a novel strategy for enhancing perovskite solar cells (PSCs) by employing 1-ethyl-3-methylimidazolium hexafluorophosphate ([EMIM]PF6) as an interface modification layer. The authors systematically investigated the impact of this ionic liquid on device efficiency enhancement, as well as on the optical and electronic characteristics of perovskite (PVK) films and NiOx/[EMIM]PF6/(Cs0.1FA0.81MA0.09PbI2.86Br0.14) perovskite interfaces, utilizing various characterization techniques. The results demonstrate that the ionic liquid facilitates dynamic interactions at the interface, enhancing the conductivity of NiOx, aligning energy levels with the PVK, and consequently facilitating charge injection and extraction. The authors' optimized PSC device achieved an impressive Photovoltaic Conversion Efficiency (PCE) of 22.11 % and a current density of 25.23 mA/cm2, demonstrating excellent stability. Notably, the modified film maintained over 75 % of its initial efficiency for more than 800 h. This work presents a straightforward and efficient approach to developing cost-effective, stable, and efficient PSCs.

The harmonic controlling schemes are very important for renewable energy applications. The power efficient applications are playing significant role in grid connected inverter applications. The measures like power factor, real & reactive power, voltage at (grid, inverter and converter side), current at (grid, inverter and converter side) are need to be improve for efficient power balancing purpose. The earlier models like liner disturbance controlling LADRC, Proportional-Resonant (PR) Control with Comb Filter (PRC-CF) and PI controlling are outdated. Therefore, PR–NF–STFT-Fuzzy logic controller is proposed to cross over the above limitations. This proposed methodology can improve the PRFI-HC dynamic response, PRFI-HC of inverter current and PRFI-HC voltage. Grid-connected inverters may use the suggested approach to emit high and low frequency harmonics. In the event of many external harmonic's sources, a different analysis statement is provided to indicate the highest achievable individual grid current harmonic. At resonance frequencies, the effect of the series damping resistor on the inverter's harmonic rejection capability is examined. The suggested harmonic structure of the converter is matched by the simulations and testing findings.

Salted duck egg yolk (SDEY) is rich in numerous beneficial nutrients, particularly proteins and fatty acids, and can be used in various edible condiments. However, uncooked SDEY can readily deteriorate when stored under ambient condition. Thus, the present study examined the changes in the shelf life of SDEY in uncooked and cooked forms stored under ambient condition (28 ± 5 °C) for 15 days. The SDEY was tested every 3 days for various physicochemical properties. The color attributes of SDEY, such as lightness (L*), redness (a*), and yellowness (b*), experienced a notable increase when cooked. This enhancement in color parameters was further amplified with ongoing storage. Additionally, the total color change (ΔE) exhibited a similar upward trend, with cooked SDEY displaying significantly higher values compared to the uncooked samples. The moisture content of SDEY was higher in uncooked samples than the cooked SDEY. On the other hand, water activity was not significantly different between the two samples. Additionally, the uncooked SDEY displayed a significantly higher pH level than the cooked SDEY. Notably, the pH values remained unaffected by the extended storage period. The total salt content was similar in both uncooked and cooked SDEY samples, yet both demonstrated a consistent decrease over extended storage periods. Similarly, all samples experienced a continuous decline in textural characteristics during the storage period. Notably, the cooked samples exhibited increased hardness, cohesiveness, springiness, and chewiness, in contrast to the uncooked SDEY, which showed a higher prevalence of adhesiveness. The total plate count and yeast and mold growth of SDEY continuously increased with storage, but cooked SDEY demonstrated better control against microbial growth. Overall, this study found that cooked SDEY is a better and more economical option for storage under ambient condition compared to the uncooked samples.

For any structures, Reinforced concrete (RC) columns are the utmost carping members. RC columns jacketed with CFRP membrane enhance resistance, deformation aptitude, and the auspicious confining impacts. Sundry scrutiny has been implemented about circular columns wrapping or veiled with CFRP layers. This scrutiny will manifest about Genetic Programming (GP) and Artificial Neural Networks (ANN) as soft computing models, mobbing exploited to ascertain the lateral confinement coefficient (Ks) valuate of RC circular columns jacketed with CFRP membrane. For the purpose to ascertain Ks value, manipulate the corresponding ambit such as the diameter of the column (b), column length (L), thickness layer of CFRP (tw), the elastic modulus of CFRP membrane (Ef) along with the compressive strength of unconfined concrete (fcc). Total collection of 142 datasets for the RC circular column was employed to decipher the prediction of Ks. Various statistical metrics (R2 = 0.933; RMSE = 0.054; NMBE = -0.001) compared with the ANN model and exhibits the superiority of GP in predicting the Ks. Taylor diagram and Rank analysis were also carried out in this article for justification. The lucidity of the GP machine enlightenment model overtures a greater sensible and exact prediction of Ks value for RC circular columns in accordance with versatile loading conditions.