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Objective: To determine the risk of post-operative hydrothorax in supra-12th rib versus infra-12th rib access in prone Mini Percutaneous Nephrolithotomy. Study Design: Quasi-experimental study. Place and Duration of Study: Armed Forces Institutes of Urology, Rawalpindi Pakistan from Jun to Dec 2022. Methodology: In our study, 110 patients underwent assessment for indication of Percutaneous Nephrolithotomy, level of access, anatomy, site of puncture, incidence of hydrothorax, and requirement of closed-tube thoracostomy. Results: Of the 110 patients who underwent prone Percutaneous Nephrolithotomy, 55(50%) patients underwent infra 12th rib tract approach (Group-1), and the rest, 55(50%) underwent supra 12th rib tract approach (Group-2) depending upon the location of stones, anatomy of pelvicalyceal system and stone burden. Overall, 10(9.09%) patients developed post- Percutaneous Nephrolithotomy Hydrothorax; out of these ten patients, 8(14.5%) patients were approached from the supra 12th rib tract and the remaining 2(3.6%) patients were approached from the infra 12th rib tract (p-value=0.047). Conclusion: The study concludes that in prone Percutaneous Nephrolithotomy, infra-12th rib tract renal access is safer than supra-12th rib renal access regarding chest complications (Hydrothorax).

Herein, five Schiff’s base analogs (designated as AK1 to AK5) were synthesized and screened for their analgesic and anti-inflammatory efficacies. All compounds exhibited analgesic activity at selected doses from 45.31 % to 75.75 %, while the standard drug diclofenac sodium produced result as 86.33 %. Compounds AK1, AK3, AK4 and AK5 were found significantly P

Objective: To compare pre-operative intravenous Cefuroxime prophylaxis to no prophylaxis regarding the development of surgical site infection in inguinal hernia mesh repair. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Surgery, Combined Military Hospital, Bahawalnager Pakistan, from Nov 2015 to Nov 2017. Methodology: A total of 230 patients were included (115 patients in each group). Group-A patients were subjected to preoperative administration of 1.5 gm intravenous Cefuroxime 1 hour before surgery, while Group B patients were subjected to no antibiotic prophylaxis following mesh repair. Development of post-operative surgical site infection (SSI) was checked till the 30th post-operative day (POD). Results: Post-operative surgical site infection was comparatively less in Group-A (3.4%) as compared to Group-B (10.4%), which was statistically significant (p-value0.038). Conclusion: Pre-operative intravenous Cefuroxime prophylaxis significantly reduces post-operative SSI following mesh inguinal hernia repair.

Background: Idiopathic hypogonadotropic hypogonadism (IHH) is a medical condition where there is a deficiency or insensitivity of gonadotropin-releasing hormone (GnRH) without a known cause. Not only are the sexual characteristics of a person affected by this condition but also are the psychological and physical development, thus necessitating its early recognition and treatment. This research was carried out to identify the laboratory parameters and to present symptoms of the patients with complaints of IHH.Materials and Methods: This retrospective, center, single-center, cross-sectional study was carried out in Aga Khan University from December 2000 until December 2020 on the patients that presented to the clinic with IHH. The patients included in the study were those that presented with hypogonadism, a low concentration of sex steroid hormone, and an abnormal gonadotropin level without any expansive pituitary or hypothalamic lesion.Results: Seventy nine patients presenting with IHH were included with their mean age of 24.2 ± 7.5 years. Of these, 64 (81.0%) had genital atrophy, 50 (63.6%) showed an absence of secondary sexual characteristics, 53 (67.1%) complained of infertility, 44 (55.7%) had not shown signs of puberty, 52 (65.8%) had erectile dysfunction, 46 (58.2%) had a decrease in libido, 11(13.9%) had a previous familial history, 24 (30.3%) had gynecomastia, 9 (11.4%) had non-descended testes, and 6 (7.6%) had anosmia. These patients had serum testosterone, luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels of 26.3 ± 60, 1.3 ± 2.4, and 2.7 ± 5.0 (IU/L), respectively.Conclusion: Thus, it can be stated that small genitalia is the most common complaint among patients with IHH, followed by infertility and lack of secondary sexual characteristics. The testosterone level in serum is also found to be low among these patients.

The inherent security and computational demands of classical consensus protocols are often presumed impervious to various forms of attack. However, quantum computing advancements present considerable threats to the assumed attack resistance of classical security strategies currently deployed within blockchain systems. Adopting consensus algorithms fortified with post-quantum security measures can significantly enhance traditional blockchains’ privacy and security dimensions. Notable advantages of such post-quantum solutions in blockchain consensus include accelerated transaction verification, clarified mining authorship, and resilience against quantum attacks. Yet, a comprehensive analysis of the implications of post-quantum solutions for blockchain consensus is notably absent in the existing scholarly discourse. This paper aims to bridge this gap by systematically reviewing Post-Quantum Blockchain Consensus (PQBC). The four primary contributions of this study are (i) a systematic approach to presenting a comprehensive overview of PQBC, (ii) the systematic selection and analysis of 29 key studies from an initial pool of 1192 papers, (iii) a critical review of methods, enhancements to security, scalability, trust, and privacy, as well as the evaluation employed for PQBC, and (iv) a discussion of primary gaps and prospective directions for future PQBC research.

Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as valuable tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets utilized by these forecasting methods, elucidating the sources and specific characteristics underpinning these datasets. Then, we rigorously conduct a performance comparison across various performance metrics, facilitating a comprehensive assessment of the efficacy of different predictive models. Notably, this comparison is carried out using the same datasets that underlie the diverse methodologies reviewed within this study, ensuring a fair and consistent evaluation. Moreover, we provide an in-depth examination of the diverse performance measures and statistical tools employed in the studies considered, providing valuable insights into the analytical frameworks used to gauge forecasting accuracy and model robustness. Lastly, we devote significant attention to the identification and analysis of prevailing challenges within the realm of electricity load and price prediction. Additionally, we delve into prospective directions for future research, thereby contributing to the advancement of this critical field.

Yu G, Ali Z, Sajjad Khan A, et al. Int J Nanomedicine. 2021;16:3255-3273. At the authors request, the Editor and Publisher of International Journal of Nanomedicine wish to retract the published article. Concerns were raised regarding the alleged duplication of images in Figures 9A, 9B and 9C with the same images from a previous article (ud Din et al, 2017 https://doi.org/10.1080/10717544.2016.1272651) by the same authors. Specifically, Figure 9A, Untreated Control, Pure Drug, HePC-NLCs, Normal Saline and all corresponding Tumor mass images appear to have been duplicated with the same images for Figure 6A, Control, Solution, DRTN, and Hydrogel images, respectively, from ud Din et al, 2017. Figure 9B, Pure Drug, Caspas-3; HePC-NLCs, Caspas-3 and Normal Saline, Caspas-3 appear to have been duplicated with the same images from Figure 6C, Solution, Ki-67 and Figure 6B, DRTN, Caspase-3 and Control, Caspase-3, respectively, from ud Din et al, 2017. Figure 9C, Untreated Control, PARP; Pure Drug, PARP; HePC-NLCs, PARP and Normal Saline, PARP appear to have been duplicated with the same images from Figure 6C, Hydrogel, CD31; Figure 6B, Solution, PARP; Figure 6C, Control, CD31 and Figure 6B, Control, PARP, respectively, from ud Din et al, 2017. The authors responded to our queries and agreed with the concerns that had been raised and requested for the retraction of the article. The Editor agreed with this decision and approved the authors retraction request. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Guo Yu, 1 Zakir Ali, 2 Anam Sajjad Khan, 2 Kalim Ullah, 3 Humzah Jamshaid, 2 Alam Zeb, 4 Muhammad Imran, 4 Sadia Sarwar, 4 Han-Gon Choi, 5 Fakhar ud Din 2 1Department of Head and Neck Breast, Xinxiang Central Hospital, Xinxiang City, Henan Province, 453000, People’s Republic of China; 2Nanomedicine Research Group, Department of Pharmacy, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan; 3Department of Zoology, Kohat University of Science & Technology, Kohat, Khyber Pakhtunkhwa, Pakistan; 4Riphah Institute of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan; 5College of Pharmacy & Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan, South KoreaCorrespondence: Fakhar ud Din Tel +92 512-90644314Fax +92 512-90644144Email fudin@qau.edu.pkHan-Gon Choi Email hangon@hanyang.ac.krBackground: The purpose of this study was to investigate the suitability of nanostructured lipid carriers (NLCs) loaded with miltefosine (HePC) as an anticancer drug for the treatment of breast cancer.Methods: HePC-NLCs were prepared using a microemulsion technique and then evaluated for particle size, polydispersity index (PDI), incorporation efficiency, in vitro release of entrapped drug, and hemolytic potential. Furthermore, pharmacokinetic, biodistribution, and liver toxicity analyses were performed in Sprague–Dawley rats, and antitumor efficacy was evaluated in Michigan Cancer Foundation-7 (MCF-7) and squamous cell carcinoma-7 (SCC-7) cells in vitro and in tumour-bearing BALB/c mice in vivo. Advanced analyses including survival rate, immunohistopathology, and terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assays were performed to evaluate apoptosis in vivo.Results: The average particle size of the HePC-NLCs was 143 ± 16 nm, with a narrow PDI (0.104 ± 0.002), and the incorporation efficiency was found to be 91 ± 7%. The NLCs released HePC in a sustained manner, and this release was significantly lower than that of free drug. The in vitro hemolytic assay demonstrated a significantly reduced hemolytic potential (∼ 9%) of the NLCs compared to that of the test formulations. The HePC-NLCs demonstrated enhanced pharmacokinetic behaviour over free drug, including extended blood circulation and an abridged clearance rate in rats. Furthermore, the HePC-NLCs exhibited higher cytotoxicity than the free drug in MCF-7 and SCC-7 cells. Moreover, the HePC-NLCs showed significantly enhanced (P < 0.005) antitumor activity compared to that of the control and free drug-treated mouse groups. Tumour cell apoptosis was also confirmed, indicating the antitumor potential of the HePC-NLCs.Conclusion: These findings demonstrate the ability of NLCs as a drug delivery system for enhanced pharmacokinetic, antitumor, and apoptotic effects, most importantly when loaded with HePC.Keywords: breast cancer, miltefosine, nano lipid carriers, bioavailability, pharmacokinetics, antitumor efficacy

The slower than expected adoption rate of blockchain technology has highlighted that there are barriers due to the diversity of its applications and its users. To overcome this limitation and take full advantage of the novel technology, researchers from academia as well as industry are dedicated to find different solutions, where two or more blockchains can interact with each other. As a result, several interoperability solutions have presented themselves. To investigate the functionalities and underlying mechanisms of interoperable blockchain solutions, researchers have conducted several surveys by discussing the features and innovations of these methods. However, the existing surveys tend to focus on the architectural description of the interoperability solutions and completely overlook the most promising aspect of blockchain adaptability, namely the smart contract. This paper fills the gap by exploring the role of smart contracts in blockchain interoperability solutions. Our research has classified the existing interoperability solutions into three main categories: heterogeneous blockchains and homogeneous smart contracts, homogeneous blockchains and homogeneous smart contracts, heterogeneous blockchains, and heterogeneous smart contracts. To provide a systematic overview of the smart contracts used in blockchain interoperability, each category is further divided into subcategories by identifying the functionalities of the smart contract used. Based on our survey, a taxonomy is proposed to help classify the blockchain interoperability solutions. The interoperability solutions in each category are analyzed in-depth, and the results are presented in tabular format to illustrate the characteristics of the interoperability solutions in a meaningful way. Finally, a number of open issues and research directions are discussed to overcome the limitations and improve the performance of blockchain interoperability.

Objective: To compare the frequency of surgical site infection, Seroma formation and urinary retention in patients undergoing Lichtenstein inguinal hernia repair using staples and polypropylene for securing the mesh. Study Design: Randomized controlled trial. Place and Duration of Study: Department of Surgery, Combined Military Hospital Rawalpindi, from Oct 2015 to Nov 2017. Methods: Patients diagnosed as cases of inguinal hernia, planned to undergo open lichenstein mesh repair were randomly divided into group A and group B having 65 patients in each group. Among group A patients, mesh was secured using skin staples while in group B patients, mesh was secured using conventional prolene 2/0 sutures. Development of postoperative surgical site infection (SSI), seromas formation and urinary retention was checked post-operatively. Results: Frequency of postoperative surgical site infection was comparatively less in group A (3.1%) as compared to group B (12.3%) which was found to be statistically significant (p-value 0.048). However, frequency of seroma formation and urinary retention was found to be insignificantly altered using both techniques (p-value 0.403 and 0.38 respectively). Conclusion: Securing mesh with skin staples is superior to prolene in term of frequency of surgical site infection. However, rate of development of seromas and urinary retention is not altered significantly using any of the techniques.

Calcium phosphate cements (CPCs) offer a promising solution for treating bone defects due to their osteoconductive, injectable, biocompatible, and bone replacement properties. However, their brittle nature restricts their utilization to non-load-bearing applications. In this study, the impact of hybrid silk fibroin (SF) and kappa-carrageenan (k-CG) nanofibers as reinforcements in CPC was investigated. The CPC composite was fabricated by incorporating electrospun nanofibers in 1, 3, and 5% volume fractions. The morphology, mineralization, mechanical properties, setting time, injectability, cell adhesion, and mineralization of the CPC composites were analyzed. The results demonstrated that the addition of the nanofibers improved the CPC mixture, leading to an increase in compressive strength (14.8 ± 0.3 MPa compared to 8.1 ± 0.4 MPa of the unreinforced CPC). Similar improvements were seen in the bending strength and work fracture (WOF). The MC3T3-E1 cell culture experiments indicated that cells attached well to the surfaces of all cement samples and tended to join their adjacent cells. Additionally, the CPC composites showed higher cell mineralization after a culture period of 14 days, indicating that the SF/k-CG combination has potential for applications as a CPC reinforcement and bone cell regeneration promoter.

Abstract In modern agriculture the use of biostimulants not only reduces the cost of production but also increase yield and quality of crops without harming environment. The response of fodder oat to biostimulants was evaluated in a pot experiment using four biostimulants at different concentrations i.e Moringa leaf extract at ratio of 1:10, 1:20, 1:30, 1:40, 1:50, Sea weed extract at 1%, 2%, 3%, 4%, 5%, Thiourea at 100 ppm, 200 ppm, 300 ppm, 400 ppm, 500 ppm and Chitosan at 25 ppm, 50 ppm, 100 ppm, 125 ppm, 150 ppm. Oat crop was treated with biostimulants using foliar application, at two growth stages i.e tillering and booting. Data on various agronomic and quality parameters were recorded and analyzed. Results showed that biostimulants significantly affected phenology and quality of oat. The highest green fodder yield (872.32 g) was observed in the pots treated with 2%Solution of seaweed extract similarly highest dry matter yield was also highest at 170.85 g Interestingly crude protein was highest for thiourea at 400 ppm. In each biostimulant following doses stood out both in terms of yield and quality. In case of moringa leaf extract the ratio of 1:40with 900 g per pot yield and 9.43 % protein, for sea weed extract 2% was better than other doses with 981 g yield and 9.17% protein, for thiourea the 400 ppm was better than other concentrations as it shows highest yield (872.32 g) and crude protein (9.52%) and for chitosan 100 ppm was comparatively better in performance with 964.98 g yield per pot and 9.35% CP. It is concluded from our study that biostimulants improve fodder oat yield and quality.

In this paper, our leading objective is to relate the fractional integral operator known as Pδ-transform with the ք-extended Mathieu series. We show that the Pδ-transform turns to the classical Laplace transform; then, we get the integral relating the Laplace transform stated in corollaries. As corollaries and consequences, many interesting outcomes are exposed to follow from our main results. Also, in this paper, we have converted the Pδ-transform into a classical Laplace transform by changing the variable lnδ−1s+1/δ−1⟶s; then, we get the integral involving the Laplace transform.

Pakistan is dealing with nutrition transition, obesity and non-communicable diseases along with children stunting, wasting and other problems of undernutrition. Ischemic Heart disease is the leading cause of death in Pakistan. Fast-Food and processed food industry's market share is increasing. They are using various strategies to exploit this large market. This paper starts with a description of the fast food industry and its rise in Pakistan. Furthermore, it suggests several policy and programs to deal with the problem of nutrition transition and obesity. This paper uses the framework of socio ecological model and social determinants of disease to suggest the intervention for the stated problems

The Langrial iron ore of Hazara area has been studied in detail at five locations for its geochemistry, genesis and economic potential investigations. A maximum of thirty samples were collected from iron ore bodies at different localities of Bagnotar, Danna Noral, TattiMaira, Najafpur and Jabri for detailed analysis. The thick bedded iron ore has been deposited in the form of unconformity at Cretaceous-Paleocene boundary sandwiched between Kawagarh and Hangu formations (i.e. Cretaceous-Paleocene boundary). At the base of Hangu Formation variable thickness of these lateritic beds spread throughout the Hazara and Kohat-Potwar plateau. These iron ore bodies are studied by X-Ray Diffraction technique (XRD), X-ray Fluorescence Spectrometry (XRF), Petrographic studies and Scanning Electron Microscope (SEM). This XRD analysis indicated that iron bearing minerals are hematite with intercalation of chamosite, quartz and a proportion of impurities having albite, illite-montmorillonite, kaolinite, calcite, dolomite and ankerite. The X-ray Fluorescence (XRF) results show that total Fe2O3 ranges from 30 to 50% having high silica, while alumina to silica ratio is less than 1. The petroghraphic studies revealed concentration of Langrial iron beds as oolitic hematite nodule having variable concentric to composite structure. A high energy shallow marine depositional environment was deduced for Langrial iron ore, as inferred from SEM and mineralogical data. The overall results show that Langrial iron ore is a low-grade iron ore with average Fe2O3of 52%, and can be upgraded by applying modern mining techniques to fulfill steel requirements of the country.

Industry 4.0 allows for greater flexibility in production processes so that products can be customized (i.e., mass customization). Innovative production techniques in an industrial liquid/yogurt filling machine (YFM) improved efficiency in the beverage industry. In this study, we have introduced the second phase designed control architecture of our YFM based on the concepts of industry 4.0 incorporating an NFC platform for improving customer satisfaction. Especially during this pandemic period, wireless technologies have been ubiquitous and pervasive for customized products. The basic components of the YFM have been described. High-level control architecture programmed fully automated filling operations, and the design stage of the development of a PFC-based controller for the YFM is elaborated. For the evaluation of the proposed control system, the operations of the electric/pneumatic input devices and actuators were simulated on FluidSIM-MecLab. The results of the simulation verify the design logic of the PFC-based controller. Comparisons were made between different production types using the developing YFM. A complex learning environment replicating a real production system to understand, learn, and apply modern manufacturing approaches has been developed. Through the creation of this YFM, the academic environment and industrial applications are combined. Consequently, the problem verification is becoming more realistic and more efficient than online (trial and error) automation programming.

Kenaf (Hibiscus cannabinus L.) is a fibrous crop, grown in tropical climate having huge biomass and can be a good candidate for the phytoremediation of different heavy metals. Consequently, the present study was conducted to explore morpho-physiological traits, photosynthetic pigments, gaseous exchange attributes, antioxidative response and phytoextraction of copper (Cu) in H. cannabinus grown under different levels of Cu i.e. 0 (control), 60, 120 and 180 µmol L−1 in Hoagland nutrient solution (pH 6.2). The results from the present study revealed that Cu toxicity reduced plant height, plant diameter, plant fresh weight, plant dry weight, photosynthetic pigments and gaseous exchange attributes compared to control. Moreover, excess Cu in the nutrient solution ameliorates contents of malondialdehyde (MDA), hydrogen peroxide (H2O2) and electrolyte leakage (EL) which showed that Cu induced oxidative damage in the roots and leaves of H. cannabinus. The oxidative stress which was induced by a high concentration of Cu in the nutrient solution is overcome by enzymatic activities of antioxidants which increased with the increase in Cu concentration, i.e. 60 and 120 µmol L−1, while the addition of Cu (180 µmol L−1) caused a reduction in the activities of superoxidase dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) in the roots and leaves of H. cannabinus. The results also demonstrated that an increase in Cu concentration in the nutrient solution causes an increase in Cu accumulation through roots, leaves and stems of H. cannabinus, although the highest Cu concentration was accumulated in roots while only a little transported to the above ground parts (leaves and stems) of the plants. All the values of bioaccumulation factor (BAF) and translocation factor (TF) were less than 1, which also indicated that a small quantity of Cu concentration is transported to the aboveground part of the plants. These findings suggested that phytotoxicity of Cu affected plant growth and biomass and increased ROS production while accumulation of Cu in different parts of plant proved that H. cannabinus is an ideal specie for phytoremediation of Cu when grown under Cu contaminated sites.

In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

The social networks become the important part of the life. During the last decade, use of social networks has increased to a great extent. People publish their content on the web they use to convey their idea by posting statuses, links, videos, images, comments(Facebook). The use of social networks rapidly increased during the last decade and there is huge amount of data publish on these networks People share their personal information and insights of their lives. In this paper we thoroughly studied five big personality models and come to the point that neuroticism reflects the criminal mind of person, we are using the text mining algorithm, which will be used to extract necessary information from the text massages (statuses, comments etc.) containing neuroticism words e.g. anger, anxiety stress, depression etc. and by using these words prediction can be made. The main aim of this paper is to find the crime tendency in person from the information they share on social networking site (FACEBOOK).