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In this review, we covered the recent advances in the synthesis of gold nanoparticles (AuNPs) and their uses in the degradation of dyes. This study provides a framework to develop a low-cost, eco-friendly, and highly efficient synthesis of AuNPs. From these synthesis methods, toxic by-products are not produced. The present study focuses on the removal of dyes by AuNPs because AuNPs act as suitable absorbents for dyes in a short time. Synthesis of AuNPs from plant extracts, e.g., marine alga, Scutellarin Barbata, Alpinia nigra, Fruit peels, Bacillus marisflavi from raw silk cocoons, amylopectin and poly acrylic acid, L. asparagine, Graphene oxide, LPEI coated AuNPs. The synthesized AuNPs were used further to remove dyes like methylene blue (MB), Rhodamine B (RB) degradation, methyl orange, acid red degradation, and Congo red.

Soil texture analysis is crucial for crop selection, fertilizer recommendation, and production. Traditional soil testing in the lab using chemicals is highly time-consuming, expensive, and risky harmful chemicals; proper equipment and trained professionals are required to get the readings and to conduct the analysis. These issues can be resolved using image processing. In this study, we proposed a Blackbox prototype machine to take images in a controlled environment under the fixed intensity of light, distance, and standard dry conditions to analyze soil texture. This innovative machine, with its efficient and precise image processing capabilities, has the potential to revolutionize soil texture analysis. Also, we marked the center points of each type of soil texture as defined in the USDA texture triangle. Hundreds of soil samples were prepared for each type according to the center point's sand, silt, and clay ratio. The image processing-based model is trained for texture analysis. This research aims to reduce the soil texture analysis time and provide a system that can do extensive analyses automatically and with accuracy. The proposed Blackbox prototype machine has proven effective in providing a controlled environment for taking images. Also, the proposed model detects soil texture with a maximum accuracy of 99.5 %. A proposed model trained on the soil samples of different texture classes available in the USDA texture triangle accurately performed texture analysis. The results benefit the recommendation of appropriate crops and fertilizers based on a given soil sample in a very short time and cost-effectively.

Abstract Lipid nanoparticles (LNPs) have proven themselves as transformative actors in chimeric antigen receptor (CAR) T cell therapy, surpassing traditional methods and addressing challenges like immunogenicity, reduced toxicity, and improved safety. Promising preclinical results signal a shift toward safer and more effective CAR T cell treatments. Ongoing research aims to validate these findings in clinical trials, marking a new era guided by LNPs utility in CAR therapy. Herein, we explore the preference for LNPs over traditional methods, highlighting the versatility of LNPs and their effective delivery of nucleic acids. Additionally, we address key challenges in clinical considerations, heralding a new era in CAR T cell therapy. Graphical Abstract

Since the internet became freely accessible everywhere in recent years. As banking transactions changed and shifted to online banking, banks paid more attention to how to make the best use of it, reduce operating expenses, and boost profitability. Online banking is a method of doing business that has frameworks that are properly connected with various customer-oriented services offered while also lowering costs and raising profits. The current study sought to investigate the impact of internet banking on Nepali banks' performance in terms of the mediating effect of training and development. It focuses mostly on how training and development affect bank performance in Nepal. Additionally, it emphasizes how crucial online banking can be for improving bank financial performance.The data were selected from 150 respondents of commercial banks located at Khathmandu, Bhaktapur, and Lalitpur. As the primary source of quantitative data for the present research, participants completed a self-administered questionnaire. Additionally, partial least squares structural equation modeling (PLS-SEM) was used to examine the data obtained through the use of questionnaires. The findings suggest a favorable association between online banking and bank performance. According to the findings, training and development significantly impact bank performance. The findings also indicate that the association between online banking and bank performance is mediated by training and development. To meet the intended profitability ratio from online banking, increase customer satisfaction, and produce strong financial performance, management should work to put focus on the training and development process.

Entrepreneurial intention is an important concept for understanding and promoting entrepreneurship. It is a strong predictor of entrepreneurial behavior, and it can lead to a number of positive benefits for individuals, society, and the economy. This study aims to examine the impact of entrepreneurial intention (EI) on entrepreneurial orientation (EO). It also examines the intervening role of environmental uncertainty (EU). The data were collected from 150 entrepreneurs from active enterprises in the 3 major cities of Pakistan. The information was gathered through a closed-ended questionnaire. Partial least squares structural equation modeling was used to analyze the data. According to the findings, EI impacts positively on EO and EU. Furthermore, EU plays a significant intervening role between EI and EO. The results of this study offer valuable information that the owners of businesses and the Ministry of Commerce may use to support entrepreneurial activities in Pakistan. This may ensure the efficient operation and elimination of organizations' failure to appropriately launch their businesses.

A low-cost adsorbent developed from unmodified Azadirachta indica leaves was used for adsorptive removal of the Congo Red dye from an aqueous medium. The adsorbent was characterized by the Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and scanning electron microscopy (SEM) techniques. For optimization of operational parameters such as dye concentration, solution pH, adsorbent dose, contact time, and temperature, batch adsorption experiments were performed. It was found that for neem leaves powder (NLP), the optimum conditions were as follows: adsorbent dose of 0.8 g, contact time of 100 min having a solution with pH value of 5, adsorbate initial concentration of 40 ppm at temperature 60 °C where maximum amount of dye, i.e., 84%, removal was observed. The process followed pseudo-first-order kinetics, which reveals physical adsorption. According to isothermal investigations, sorption data were best fit with the Freundlich isotherm model. Thermodynamically, the adsorption of the Congo Red dye by the neem leaf powder was exothermic. Furthermore, the mechanistic removal of the Congo Red dye by the NLP has been explored with the help of the surface complex formation (PHREEQC) mechanism. Overall, the results of the study explore the promising nature of NLP for Congo Red dye removal. HIGHLIGHTS Providing clean water to the population is one of humanity's main challenges.; Adsorption is one of the most cost-effective and promising techniques for dye removal from polluted water.; 84% of Congo Red dye removal takes place at optimized conditions.; Isotherm, kinetic, and thermodynamics were studied.; The promising nature of the developed system was checked with tap water.;

Time synchronization among smart city nodes is critical for proper functioning and coordinating various smart city systems and applications. It ensures that different devices and systems in the smart city network are synchronized and all the data generated by these devices is consistent and accurate. Synchronization methods in smart cities use multiple timestamp exchanges for time skew correction. The Skew Integrated Timestamp (SIT) proposed here uses a timestamp, which has time skew calculated from the physical layer and uses just one timestamp to synchronize. The result from the experiment suggests that SIT can be used in place of multiple timestamp exchanges, which saves computational resources and energy.

Autophagy is an evolutionarily conserved cellular recycling process that maintains cellular homeostasis. Despite extensive research in endocrine contexts, the role of autophagy in ovarian and testicular steroidogenesis remains elusive. The significant role of autophagy in testosterone production suggests potential treatments for conditions like oligospermia and azoospermia. Further, influence of autophagy in folliculogenesis, ovulation, and luteal development emphasizes its importance for improved fertility and reproductive health. Thus, investigating autophagy in gonadal cells is clinically significant. Understanding these processes could transform treatments for endocrine disorders, enhancing reproductive health and longevity. Herein, we provide the functional role of autophagy in testicular and ovarian steroidogenesis to date, highlighting its modulation in testicular steroidogenesis and its impact on hormone synthesis, follicle development, and fertility therapies.

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

In this paper, a decentralized consensus algorithm for a robotic swarm is presented, which enable agents to escape collisions and avoid obstacles collectively. To achieve consensus, we have used neighboring clusters and extreme function for data dissemination and consensus in agents to avoid obstacles respectively. The main challenge is to make fast and accurate collective decision by improving data propagation in presence of Byzantine agents. To improve data dissemination in local interactions of agents in decentralized fashion, an iterative rumor-based data propagation model is proposed. Due to presence of Byzantine robots, the LCP and the W-MSR algorithm cannot achieve consensus for obstacle avoidance in artificial swarms. We establish the Expectation-based Extreme Value (EEV) algorithm using the local expectation and the extreme function to solve these problems. The experiments conducted in simulations demonstrate that the rumor spreading method has better results than the Peer-to-Peer method in randomly connected swarm signaling network (SSN) with complex environmental circumstance, the EEV algorithm is more effective than the LCP and the W-MSR for the swarm navigation and consensus in agent on large obstacles / environmental features. Furthermore, in presence of malicious / hacked agents in a swarm it is very difficult to reach consensus. The result show that proposed algorithm can handle the Byzantine agents effectively.

The self-directed career attitude encompasses the willingness to take ownership and control of one's career path and actively steer one's professional growth and advancement. The present study aims to investigate the influence of self-directed career attitude (SDCA) on employee job satisfaction (EJS). This study additionally investigates the mediating function of leadership in the relationship between self-directed career attitude (SDCA) and employee job satisfaction (EJS). Four hypotheses were formulated in order to examine both the direct and indirect connections. The primary data was obtained by administering a questionnaire and then analyzed using partial least squares structural equation modeling. The demographic information was analyzed using the statistical package for the social sciences. The theoretical framework employed in this study was self-determination theory. The test results indicate a significant positive correlation between SDCA, leadership, and EJS. The findings also suggest a noteworthy positive mediating influence of leadership in the relationship between SDCA and EJS. This study aims to enhance our understanding of the causes and outcomes of human behavior on human resources, hence playing a crucial role in facilitating efficient career management and formulating suitable HR policies.

Background/Objectives: As a primary source of mortality and disability, bacterial infections continue to develop a severe threat to humanity. Nuclear medicine imaging (NMI) is known for its promising potential to diagnose deep-seated bacterial infections. This work aims to develop a new technetium-99m (99mTc) labeled tigecycline radiopharmaceutical as an infection imaging agent. Methods: Reduced 99mTc was used to make a coordinate complex with tigecycline at pH 7.7–7.9 at room temperature. Instantaneous thin-layer chromatography impregnated with silica gel (ITLC-SG) and ray detector equipped high-performance liquid chromatography (ray-HPLC) was performed to access the radiolabeling yield and radiochemical purity (RCP). Results: More than 91% labeling efficiency was achieved after 25 min of mild shaking of the reaction mixture. The radiolabeled complex was found intact up to 4 h in saline. Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) infection-induced rats were used to record the biodistribution of the radiopharmaceutical and its target specificity; 2 h’ post-injection biodistribution revealed a 2.39 ± 0.29 target/non-target (T/NT) ratio in the E. coli infection-induced animal model, while a 2.9 ± 0.31 T/NT value was recorded in the S. aureus bacterial infection-induced animal model. [99mTc]Tc-tigecycline scintigraphy was performed in healthy rabbits using a single photon emission computed tomography (SPECT) camera. Scintigrams showed normal kidney perfusion and excretion into the bladder. Conclusion: In conclusion, the newly developed [99mTc]Tc-tigecycline radiopharmaceutical could be considered to diagnose broad-spectrum bacterial infections.

This research study represents a polydexterous deep reinforcement learning-based pick-and-place framework for industrial clutter scenarios. In the proposed framework, the agent tends to learn the pick-and-place of regularly and irregularly shaped objects in clutter by using the sequential combination of prehensile and non-prehensile robotic manipulations involving different robotic grippers in a completely self-supervised manner. The problem was tackled as a reinforcement learning problem; after the Markov decision process (MDP) was designed, the off-policy model-free Q-learning algorithm was deployed using deep Q-networks as a Q-function approximator. Four distinct robotic manipulations, i.e., grasp from the prehensile manipulation category and inward slide, outward slide, and suction grip from the non-prehensile manipulation category were considered as actions. The Q-function comprised four fully convolutional networks (FCN) corresponding to each action based on memory-efficient DenseNet-121 variants outputting pixel-wise maps of action-values jointly trained via the pixel-wise parametrization technique. Rewards were awarded according to the status of the action performed, and backpropagation was conducted accordingly for the FCN generating the maximum Q-value. The results showed that the agent learned the sequential combination of the polydexterous prehensile and non-prehensile manipulations, where the non-prehensile manipulations increased the possibility of prehensile manipulations. We achieved promising results in comparison to the baselines, differently designed variants, and density-based testing clutter.

The self-directed career attitude involves assuming responsibility and authority over one's career trajectory and actively directing one's professional development. This study examines the impact of self-directed career attitude (SDCA) on organizational citizenship behavior (OCB). This study also examines the mediating role of trust between the self-directed career attitude and OCB. Four hypothesis was developed to test the direct and indirect relationship. Primary data was collected through a questionnaire and analyzed through partial least square structural equation modeling. The SPSS was used to analyze the demographic information. The underpinning theory used in this study was self-determination theory. The test results show the positive direct relationship of SDCA, OCB and trust. The results also show the significant positive mediating effect of trust between SDCA and OCB. This research contributes to comprehending the mechanisms and consequences of behavior on human resources, which is essential for effective career management and developing appropriate HR policies.

Abstract Background Radioactive iodine 131 (I-131) is used in the treatment of differentiated thyroid cancer after thyroidectomy; however, its accumulation in non-thyroidal regions may give false positive results on iodine whole body scan (WBS). Only a few cases of radioiodine uptake in the orbital region in patients with thyroid cancer have been reported, which could be due to metastasis or false positive causes resulting from contamination or inflammatory etiology. Case presentation We describe a case of 49-year-old male with history of papillary thyroid carcinoma. The follow-up I-131 whole body scan showed a focus of increased radiotracer uptake in the region of left orbit. Correlative single photon emission computed tomography (SPECT-CT) showed that uptake was due to stenosis of the left nasolacrimal duct causing obstructive symptoms on left side. Dacryoscintigraphy also demonstrated significant obstruction of left nasolacrimal system. Conclusions The nasolacrimal duct obstruction is a rare adverse event after I-131 therapy that can result in an accumulation of radioiodine on whole body scan, mimicking metastasis. The use of additional spot views and SPECT-CT images along with thyroglobulin levels can help physicians in clarifying the situation in these uncertain cases.

Bacillus thuringiensis (Bt) toxins are potential alternatives to synthetic insecticides for the control of lepidopteran pests. However, the evolution of resistance in some insect pest populations is a threat and can reduce the effectiveness of Bt toxins. In this review, we summarize the results of 161 studies from 20 countries reporting field and laboratory-evolved resistance, cross-resistance, and inheritance, mechanisms, and fitness costs of resistance to different Bt toxins. The studies refer mainly to insects from the United States of America (70), followed by China (31), Brazil (19), India (12), Malaysia (9), Spain (3), and Australia (3). The majority of the studies revealed that most of the pest populations showed susceptibility and a lack of cross-resistance to Bt toxins. Factors that delay resistance include recessive inheritance of resistance, the low initial frequency of resistant alleles, increased fitness costs, abundant refuges of non-Bt, and pyramided Bt crops. The results of field and laboratory resistance, cross-resistance, and inheritance, mechanisms, and fitness cost of resistance are advantageous for predicting the threat of future resistance and making effective strategies to sustain the effectiveness of Bt crops.

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem’s convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark.

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency.

This review explores the integration of graphitic carbon nitride (g-C3N4) with model drugs and diverse formulations to obtain nanocomposites with potential for cancer therapy. Beyond the synthesis, the study also deals with cancer-affected organs, elucidates mechanisms of drug action and categorizes g-C3N4-based anti-cancer compositions. The responsive elements contributing to cancer inhibition under the chemotherapeutic influence include reactive oxygen species (ROS), mitochondrial potential, oxidative stress, magnetic responsiveness, profound thermal and photo energy penetration, metal retention toxicity, adenosine triphosphate (ATP) blockade in cancer cells, insulating microenvironments within tumours and immune-modulating antibodies. Notably, breast, prostate, lung, ovary and stomach cancers owe their genesis exclusively to abnormal cell proliferation. Our review reveals that the integration of model drugs (MD) with metal ions (MI) on g-C3N4 (g-C3N4/MDMI) shows enhanced biological activity, compared to metal ions and model drugs alone. The paper refers to several characterization techniques to decipher intricate data patterns and facilitate explanations of in vitro analyses focused on cancer cell viability and proliferation. Upon analysis of all data, g-C3N4 emerges as a compelling drug carrier, particularly within the anticancer drug delivery systems. This review not only emphasizes the immense potential of g-C3N4 nanocomposites but also paves the way for future advancements in effective cancer treatments.

In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

Rich nature of social media data offers a great opportunity to examine social worlds of its users. Further to wide range of topics being discussed on social media, alcohol-related content is prevalent on social media and studies have found an association between this content and increased consumption of alcohol, cravings for alcohol and addiction. This study analyses social media data to examine social worlds of risky drinking in Victoria, Australia. This study conducted a scoping literature review and two online surveys, one with the general community and the other with health professionals, to determine key words to search for on social media sites. These keywords were used in a social media analytics tool called Talkwalker to generate quantitative and qualitative data on the social media users and their conversations. NVIVO was used for developing categories and themes in a sample of 172 posts. A total of 1,021 results were obtained from Twitter. The main demographic group found to be involved in conversations about drinking alcohol on Twitter was young fathers aged 25-34 years. The culture of alcohol consumption in Victoria for Twitter users is reflective of Australia's drinking culture within which risky drinking, and in particular binge drinking, is normalised.

Brassiere straps are commonly perceived as one of the most irritating and uncomfortable components of a brassiere, especially for women with heavier breasts. This study aimed to design and developed auxetic structures to enhance the ergonomics of intimate apparel. The brassiere straps were developed by using textured polyester and nylon yarns with auxetic weave structures in two different widths. Tests were conducted to evaluate Poisson’s ratio, tensile strength, and pressure distribution properties and overall ergonomics of developed straps. Subjective analysis was also performed by conducting wear trials. The development of polyester straps yielded excellent results compared to nylon. In the subjective analysis, the selected polyester sample consistently performed better than others, significantly enhancing ergonomics comfort, fit, and psychological comfort according to respondents’ preferences. Based on the results, Auxetic structure can be a better alternative for brassiere straps in active wear to avoid related health problems and to improve women’s experience.

Background: In contemporary times, women have gained recognition as accomplished entrepreneurs who make substantial contributions to economic development by virtue of their perseverance, excellence, and expertise. The concept of women's entrepreneurship encompasses the undertaking of creating novel enterprises, with the individual's intention to assume the role of an entrepreneur serving as a pivotal factor in this developmental trajectory. The underperformance of female entrepreneurs is alarming for developing nations. Purposefully, exploring the factors correlating female entrepreneurship intention is highly regarded, and a timely research is needed, thus this study explores the context of Pakistan considered a developing country. Hence, we collected data from listed small and medium enterprises (SMEs) operating in the country by means of structured questionnaires. Using SPSS and Smart-PLS software, we developed a conceptual model and performed analysis. The Theory of Planned Behavior (TPB) constitutes a robust psychological framework applicable to understanding and anticipating various human behaviors, first and foremost, entrepreneurial intention. When applying the TPB to the area of women's entrepreneurial intentions, several crucial factors come into play, including motivation, self-confidence, personality traits, and adaptability. The results indicate that factors such as the will to start a business, willingness, personality traits, adaptability, and background factors, including education and experience, impact entrepreneurial intention. Also, their professional careers make them able to launch a business. Based on our findings, we recommend that the Small and Medium Enterprise Development Authority (SMEDA), policymakers, and practitioners may provide various incentives and support systems related to these factors to encourage their own company launches. Prior research has examined various factors that impact the entrepreneurial mindsets of women. However, our study focuses specifically on closely associated aspects that have received limited attention, particularly in the context of Pakistan. This study makes a valuable contribution to the extant body of literature by providing empirical evidence in the aforementioned domains.

A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the base of automation. However, the increased number of sensors make the IoT networks more complex and scaled. Due to the increasing size and complexity, IoT networks of scale-free nature are found highly prone to attacks. In order to maintain the functionality of crucial applications, it is mandatory to increase the robustness of IoT networks. Additionally, it has been found that scale-free networks are resistant to random attacks. However, they are highly vulnerable to intentional, malicious, deliberate, targeted and cyber attacks where nodes are destroyed based on preference. Moreover, sensors of IoT network have limited communication, processing and energy resources. Hence, they cannot bear the load of computationally extensive robustness algorithms. A communication model is proposed in this paper to save the sensors from computational overhead of robustness algorithms by migrating the computational load to back-end high power processing clusters. Elephant Herding Robustness Evolution (EHRE) algorithm is proposed based on an enhanced communication model. In the proposed work, 6 phases of operations are used: initialization, sorting, clan updating, clan separating,selection and formation, and filtration. These process collectively increase the robustness of the scale-free IoT networks. EHRE is compared with well-known previous algorithms and is proven to be robust with a remarkable lead in performance. Moreover, EHRE is capable to achieve global optimum results in less number of iterations. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than Enhanced Differential Evolution (EDE) algorithm, 65.22% better than Genetic Algorithm (GA), 86.35% better than Simulating Annealing (SA) and 94.77% better than Hill climbing Algorithm (HA).

Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net, and ResNet can be considered as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant, usually named U-Net Segmentation, has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw in high memory and CPU consumption while moving the whole feature map to the corresponding decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problems with the efficient use of memory consumption. The proposed model has been evaluated on a standard benchmark dataset of Eye Net, having 32 classes of retinal diseases. From experimental evaluation, it has been concluded that the proposed model performs better regarding memory management and accuracy. The overall comparison has been performed based on precision, recall, and accuracy with different numbers of epochs and time consumption by each step. The proposed technique achieved an accuracy of 95% on the Eye-net dataset.

Breast cancer is one of the most prevalent causes of death among women across the globe. Early detection is the best strategy for reducing the mortality rate. Currently, mammography is the standard screening modality, which has its shortcomings. To complement this modality, thermal infrared-based Computer-Aided Diagnosis (CADx) tools have been presented as economical, less hazardous, and a suitable solution for various age groups. Although a viable solution, most CADx systems are built primarily from frontal breast thermograms, and are likely to miss lesions that may develop on the sides. Additionally, these systems often disregard critical clinical data, such as risk factors. This paper presents a novel CADx system that utilizes deep learning techniques for breast cancer detection. The system incorporates multiple breast thermogram views and corresponding patient clinical data to improve the accuracy of the diagnosis. We describe the methodology of the system, including the extraction of regions of interest from images and the use of transfer learning to train three different models. We evaluate the performance of the models and compare them to similar works from the literature. The results demonstrate that using multi-inputs outperforms single-input models and achieves an overall accuracy of 90.48%, a sensitivity of 93.33%, and an AUROC curve of 0.94. This approach could offer a more cost-effective and less hazardous screening option for breast cancer detection, particularly for a wide range of age groups.

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

Objective: To determine the effects of ionizing radiations on trace metal levels in healthcare workers exposed to occupational radiation in a tertiary care health facility. Study Design: Comparative cross-sectional study. Place and Duration of Study: Department of Chemical Pathology & Endocrinology, Armed Forces Institute of Pathology, Rawalpindi Pakistan, in collaboration with the Department of Radiology, Combined Military Hospital, Rawalpindi Pakistan, from Apr to Oct 2021. Methodology: Healthcare workers (n=45) exposed to occupational radiations were compared with an equal number of Controls in a tertiary care health facility for their trace metal levels. An atomic absorption spectrophotometer (AAS) was used to measure serum zinc (Zn) and copper (Cu) levels. A calorimetric technique was used to measure serum iron (Fe) levels. Results: Mean serum Copper (9.54±2.52 µmol/l) and Zinc (11.78±1.90 µmol/l) concentrations of the Risk-Group were significantly lower than their respective Control-Groups (Copper: 14.76±3.13 µmol/l and Zinc: 14.67±3.01 µmol/l). At the same time, mean serum Iron levels in the Exposed-Group (17.55±3.88 µmol/l) were significantly (p

As the global population raises, developing countries seem to have issues with the productivity of the construction projects due to high demand. Therefore, this paper aims to examine the effect of extrinsic diversity aspects impacting the construction worker efficiency. The data collection was made by a questionnaire sent to construction stakeholders working in the Punjab region of Pakistan. A comparison was drawn based on varied employee characteristics i.e., experiences and ages. It was found that old workers were negatively impacted by the distant location of the construction site. The young employees were most inclined towards holidays when working away from home. The access to basic amenities during a construction project was regarded as a major setback for the workers' productivity. Experienced workers preferred working within the vicinity and easy access to their houses. Based on the ethnicity factor while selecting the workforce, newcomers faced hurdles while entering the construction sector.

Abstract Extracellular RNAs (exRNAs) are novel circulating factors that can be used as biomarkers in various diseases. Their unique and diverse kinds, as well as their role as biomarkers, make them significant biomarkers. There has been immense work carried out since the discovery of exRNAs in circulation and other biological fluids to catalog and determine whether exRNAs may be utilized as indicators for health and illness. In this review, we aim to understand the current state of exRNAs in relation to various diseases and their potential as biomarkers. We will also review current issues and challenges faced in using exRNAs, with clinical and lab trials, that can be used as viable markers for different diseases.

Chimeric antigen receptor (CAR) T cell therapy for solid tumors shows promise, but several hurdles remain. Strategies to overcome barriers such as CAR T therapy-related toxicities (CTT), immunosuppression, and immune checkpoints through research and technology are needed to put the last nail to the coffin and offer hope for previously incurable malignancies. Herein we review current literature and infer novel strategies for the mitigation of CTT while impeding immune suppression, stromal barriers, tumor heterogeneity, on-target/off-tumor toxicities, and better transfection strategies with an emphasis on clinical research and prospects.

This study aims at the proposition of a novel strategy to classify the upper body silhouette of the male population into different shapes. Instead of using an existing measuring scheme based on linear evaluation of the body, we devise the use of angle-based anthropometric data to capture the exhibited curvature of the upper body. The objectives are attained by quantifying and employing the chest-waist (CW) angle and hip-waist (HW) angle in the instigation of a more appropriate classification of the upper body. A sample of 241 males aged 17–29 years was scanned through a 3D body scanner to gather the anthropometric data including chest girth, waist girth, and hip girth and their respective heights. In the next phase, the extracted information was used to calculate the angles. Based on the empirical realizations of the resulting indices, criteria defining the classifications of the body shape were determined. The operational environment is further enriched by considering varying levels of angular differences capable of offering more notable stratification of the upper male body shapes. It is realized that the angular difference of 10 ° between the CW and HW angles results in the more prominent classification of the upper male body. The proposed body classification technique classifies the sample of 241 males into three body shapes that are, straight (61%), hourglass (36.51%), and torch shape (2.48%). The classification scheme proposed in this study is a step toward achieving a high degree of customer satisfaction regarding clothing fit. It is a reference for coming research in body shape classification to achieve clothing fit in customized clothing.

Extracellular Vesicles (EVs) have gained high repute in drug delivery systems owing to their relatively higher efficacy as natural drug delivery vehicles. The current literature has advanced the use of EVs in drug delivery through exploring various aspects including their biogenesis, characterization, and nanoengineering techniques thereby leading them from laboratory to clinical use with optimized good laboratory practices. In this timely review, we summarize the current status of EVs characterizations and recent updates on nanoengineering of EVs regarding Cargo loading and surface fabrication. Further, we have also reviewed current progress in clinical translation and implications of EVs in clinical trials together with future recommendations.

Piezoelectric materials have attracted more attention than other materials in the field of textiles. Piezoelectric materials offer advantages as transducers, sensors, and energy-harvesting devices. Commonly, ceramics and quartz are used in such applications. However, polymeric piezoelectric materials have the advantage that they can be converted into any shape and size. In smart textiles, polyvinylidene fluoride (PVDF) and other piezoelectric polymers are used in the form of fibers, filaments, and composites. In this research, PVDF nanofibers were developed and integrated onto a knitted fabric to fabricate a piezoelectric device for human body angle monitoring. Scanning electron microscopy and X-ray diffraction analyses were used to study the morphology and to confirm the beta phase in fibers. The results reveal that the nanofibers made from solutions with high concentration were smooth and defect-free, compared to the fibers obtained from solutions with low concentration, and possess high crystallinity as well. Under high dynamic strain more output voltage is generated than under low dynamic strain. The maximum current density shown by the device is 172.5 nA/cm2. The developed piezoelectric nanofiber sensor was then integrated into a knitted fabric through stitching to be used for angle measurement. With increasing bending angle, the output voltage increased. The promising results show that the textile-based piezoelectric sensor developed in this study has a great potential to be used as an angle measuring wearable device for the human body due to its high current density output and flexibility.

Chimeric antigen receptor (CAR) T cell treatment is an emerging subject following its curative response in hematological metastasis. However, solid tumors present a number of obstructions which have been a bull's eye to steer the CARs toward another victory in solid tumor microenvironment (TME). To combat against solid tumors, the construction, transfection and delivery of CARs is obliged to nano-engineering for better results and success in clinical trials. Herein, in this minireview, we discuss some of the potential and novel applications of nanotechnology to engineer better performing CARs to target solid TME. Moreover, we highlight potential gaps and strategies to overcome for future advancements in nano immunoengineering.

Short-term load forecasting plays an essential role in the efficient management of electrical systems. Building an optimization model that will enhance forecasting accuracy is a challenging task and a concern for electrical load prediction. Due to Artificial Neural Networks (ANNs), the final result depends on initial random weights and thresholds that affect the stability of the forecast. Although much devotion is being given to improving the forecast accuracy, convergence, complexity, and resilience need to be considered for stable predictive models. To overcome this limitation, this work has jointly considered the Wavelet Neural Network (WNN) and Self-Adaptive Momentum Factor (SAMF) to achieve fast convergence, stability, and high accuracy. The proposed hybrid model is developed by combining the Feature Engineering (FE) and SAMF with the WNN model. The FE removes the irrelevant data and shallow features to ensure high computational performance. In contrast, the SAMF combines the wavelet transform’s time and frequency domain properties and adjusts the WNN model’s corresponding parameters. This ensures the global optimum solution while returning accurate predictive results. Finally, the SAMF is used to tune the control parameters of WNN by initializing the random weights and thresholds to accelerate the convergence rate and improve the accuracy compared to the Back-Propagation (BP) method. The proposed hybrid model is tested on the real-time datasets taken from the Australian states of (New South Wales (NSW), and Victoria (VIC)). Experimental results show that the developed model outperforms other benchmark models such as WNN-IGA, BPNN, WNN-AMBA, and Enhanced WNN in terms of instability, rate of convergence, and accuracy.

With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

Given the recent trends in the MPPT converters in PV systems, which have been researched extensively to improve design, modified closed-loop converter technology based on SoC is presented here. This paper aims to provide detailed information on the modern-day solar Maximum Power Point Tracking (MPPT) controller and Battery Management System (BMS). Most MPPT controller examination researched in the past is suitable only for fixed-rated battery capacity, which limits the converter capability and applications. The proposed paper uses the distributed energy management control technique to dispatch multi-battery charging based on the State of Charge (SoC). The converter construction is modified here as per the prerequisite of the model. The system hardware is developed and tested using Atmega2560 low power RISC based high-performance microcontroller. The batteries' SoC level and State of Health (SoH) are calculated using embedded sensors and communication platforms through the IoT platform and Global System Monitoring (GSM) technology. The GSM and IoT technology ensure that the different batteries are monitored periodically, and any irregularities are immediately addressed through the distributed energy management control technique. This ensures a safe, reliable, and effective charging of multiple batteries with increased accuracy, thereby maximizing battery life and reducing operational costs.

Sixteen fuberidazole derivatives as potential new anticancer bioreductive prodrugs were prepared and characterized. The in vitro anticancer potential was examined to explore their cytotoxic properties by employing apoptosis, DNA damage, and proliferation tests on chosen hypoxic cancer cells. Eight substances (Compound 5a, 5c, 5d, 5e, 5g, 5h, 5i, and 5m) showed promising cytotoxicity values compared to the standard control. The potential of compounds was also examined through in silico studies (against human serum albumin), including chem-informatics, to understand the structure-activity relationship (SAR), pharmacochemical strength, and the mode of interactions responsible for their action. The DFT calculations revealed that only the 5b compound showed the lowest ΔET (2.29 eV) while 5i showed relatively highest βtot (69.89 x 10-31 esu), highest αave (3.18 x 10-23 esu), and dipole moment (6.49 Debye). This study presents a novel class of fuberidazole derivatives with selectivity toward hypoxic cancer cells.

This study is aimed at investigating the motivational needs of Pakistani Facebook users and intended to develop a scale to measure motivation to use Facebook among Pakistani users. Opting uses and gratifications theory and reviewing the relevant literature, the researchers developed a scale comprised of six motivations. A cross-sectional survey was conducted with the university students, and 1245 university students participated in this study. The results of first- and second-order confirmatory analyses indicate that the scale was reliable and had construct validity as well. This scale confirmed six motivations as motivational needs of Pakistani Facebook users. This scale covers both social and personal needs. The findings of this study confirm that Facebook users utilize Facebook for various purposes.

Objectives: It was a community based, cross sectional study undertaken to access the frequency of HCV infection and to find out risk factor accociated with its spread.. Results: The frequency of HCV was found to be 53.6%.The most important risk factore associated with the transmission of HCV infection was unsafe injection therapy with contaminated equipment.

In visual inspection, the impact of different human factors on inspection performance has been evaluated in past. However, there is a need to study the effect of relevant factors and their interrelation in a single framework to monitor and improve inspection performance. This study aims to identify and evaluate such human factors for proposing a framework that indicates their interrelationship. After reviewing the literature, five constructs with their respective observed variables are selected to propose the framework. The survey instrument is developed using the suggested framework to collect data from industry professionals where human labor perform the inspection for products such as textiles. Finally, data are collected through an online survey and analyzed using confirmatory factor analysis to test the hypotheses. The results verify that the selected constructs are good measures of inspection skill, however, some variables are excluded from the model for being insignificant. Results show that the factors related to the constructs – personal, physical/mental, and organizational – are found more influencing than system and inspection-task factors. This study identifies and models the inspection-related significant factors into a framework that will help monitor and improve the performance of an individual or whole inspection station.

Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The “5.12” Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process.

BackgroundValue-driven career attitude (VDCA) is considered a dimension of a protean career attitude (PCA). Individuals with this attitude seek out personally meaningful experiences and set their own psychological career success standards. This study investigates the association between value-driven career attitude and job performance. It looks at how organizational citizenship behavior affects the relationship between value-driven career attitudes and job performance.MethodsA self-reported questionnaire was used to collect data from 400 random employees of SMEs in Pakistan during the early pandemic. We chose Cochran’s formula to determine the appropriate sample size, and PLS-SEM was used to analyze the model. P-O fit and self-determination theory is the theoretical lenses used in this study. The underpinning theories to this study enable the researchers to establish a link between VDCA, OCB, and job performance.ResultsBy analyzing a sample of 400 employees from active enterprises, we discover that VDCA contributes to an improvement in job performance. Furthermore, OCB plays an intervening effect in the relationship between VDCA and job performance. Thus, the study provided evidence for the underpinning models of P-O fit and self-determination theory.ConclusionThis study adds to the body of knowledge by investigating the connections between VDCA, OCB, and job performance in SMEs. The existing literature sheds scant light on these linkages, leaving a gap that this study will address. The current study expands on other themes to provide an in-depth analysis of many under-explored PCA outcomes, which may open up new avenues for future researchers to broaden and strengthen PCA with other constructs.

Interleukin-12 is a heterodimeric cytokine having many family members IL-23, IL-27, IL-35, and recently discovered IL-39, produced by macrophages and dendritic cells. IL-12 is an effector cytokine and engages in anti-tumor and many immunotherapies as IL-12 DNA expression, Th1 helper cells, and natural killer cells. IL-12 stimulates interferon γ (INF-γ) synthesis by the activation of STAT4 which then differentiates the Th1 helper cells with the T-bet transcription, playing an important role in cancer treatment. This review focuses on the role of IL-12 family as an antitumor agent as understanding the biology and molecular mechanism of IL-12 family members is crucial for designing new therapeutic strategies against different cancers.

Cell-free DNA (cfDNA) are un-encapsulated DNA fragments present in biological fluids ranging in an average size of up to 200 base pairs. The novel use of cfDNA is a prime candidate in the diagnostic and prognostic approach to unveiling many inflammatory diseases, especially cancer. Moreover, their potential as biomarkers is due to their ubiquitous presence in the body, non-invasive nature, and aiding in a different autopsy method. This review will focus on the diagnostic and prognostic potential of cfDNA as non-invasive biomarkers in oncology.

Life cycle cost analysis (LCCA) is an effective indicator to evaluate the economic sustainability of construction projects. LCCA implementation in the Malaysian construction industry remains limited due to its unawareness. This research aims to examine the LCCA awareness and implementation and evaluate the factors that promote LCCA implementation. A survey of 320 Malaysian construction professionals was conducted, followed by statistical data analysis including frequency, mode and relative importance index. According to the findings, 20.1% of newly hired employees in consultancies and contruction firms have minimum education and no awareness, 20%–70% had average awareness with some experience in the field and higher degrees, and 4.4% had a high level of awareness with extensive experience in the field. This study provides an in-depth glance into LCCA knowledge, awareness, implementation and factors that can enhance the LCCA implementation in Malaysia and other developing nations.