Economic Evaluation of Placer Gold Along River Indus From Ghazi to Kund, Khyber Pakhtunkhwa, Pakistan: Implications for Commercial Scale Pilot Plant

A detailed study was conducted on Indus river in order to evaluate the economic potential. Samples werecollected for geochemical studies from both stream sediments (SS) and heavy mineral concentrates (HMC) along Indusriver from Ghazi, Hund, Beka, Alladher and Kund in Khyber Pakhtunkhwa province. All the samples were analyzed forgold (Au), silver (Ag) and base metals in order to get information about geochemical associations. Panning and sluicemethods were applied for the collection of these samples and extraction of visible gold. Results show that HMC havehigh Au contents, having maximum value of 44.15 g/t and 39.15 g/t in Kund and Beka areas respectively with anaverage value of 15.18 g/ton and 11.37 g/ton while the SS have average amount of Au as 0.8 g/t. In both these mediahigher concentration of gold was found in HMC. On the basis of results from both sampling media, the placer golddeposits in Kund, Alladher and Beka are considered highly economical for the commercial exploration of placer gold.Overall, the HMC derived from silt, sand, gravels and cobbles along Indus river indicated potential for placer gold thatcan be utilized for extraction of gold at commercial scale

Impact of Project Leadership on Project Success: Mediated by Project Governance and Moderated by Goal Clarity

This study investigates the connections between project leadership, governance, goal clarity, and project success in Pakistan's Civil Engineering (Private) sector. Data was collected from employees using a quantitative approach. A questionnaire was distributed to gather information on project governance, goal clarity, and project success. The results showed a strong relationship between project leadership and project success, with a robust governance framework impacting project leadership effectiveness. Goal clarity moderated the relationship, emphasising the importance of well-defined objectives. The study emphasises the critical role of project leadership in Pakistan's civil engineering sector, emphasising the importance of solid project management frameworks and clear objectives. This information is crucial for organisations and project managers seeking to improve project performance in the dynamic environment of Pakistani civil engineering projects

Olfactory function in migraine both during and between attacks.

INTRODUCTION: People with migraine often report being osmophobic, both during and between acute migraine attacks. It is not clear, however, whether such reports are associated with changes in olfaction such as hyperosmia, as measured by psychophysical testing. In this case-control study we quantitatively assessed olfactory identification ability, which correlates with threshold tests of olfactory acuity, in patients with migraine at baseline (no headache), during migraine episodes, and after a treated attack and compared the test scores to those of matched control subjects. METHODS: Fifty episodic migraine subjects and 50 and sex- and age-matched controls without headache were tested. All completed the University of Pennsylvania Smell Identification Test (UPSIT), a standardized and well-validated olfactory test. RESULTS: At baseline, the UPSIT scores did not differ significantly between the migraine and control study groups (median paired score difference: -1, p = 0.18). During migraine attacks, a minority of migraine subjects (eight of 42) developed microsmia (i.e. lower test scores by at least four points), suggesting that, as compared to their matched controls, olfactory acuity was somewhat impaired during migraine attacks (p = 0.02). This difference was less pronounced and not statistically significant after a successfully treated attack (p = 0.15). DISCUSSION: People with episodic migraine were found to have similar olfactory function as age- and sex-matched controls, but a minority exhibit microsmia or hyposmia during acute attacks. The cause of this dysfunction is unknown, but could relate to autonomic symptoms, limbic system activation, or disorders of higher order sensory processing

Simulation, synthesis, and analysis of strontium-doped ZnO nanostructures for optoelectronics and energy-harvesting devices

The demand for clean and sustainable alternative energy resources is linearly increasing day by day due to the prevailing electricity crisis. Small-scale energy harvesting is considered a sustainable way to generate clean energy. Advanced energy solar cells, mainly dye-sensitized solar cells use solar energy and convert it into electrical energy. Similarly, MEMS-based piezoelectric materials are used to convert mechanical energy into electrical energy. For these applications, zinc oxide is considered one of the most suitable materials with high conductive, tunable band gap, and piezoelectric properties. However, altering these properties can be carried out by the addition of metal and other materials. Various research work has been carried out to study the addition of conductive metal as a dopant to alter the properties of zinc oxide. In this study, Strontium has been doped in ZnO to form a nanostructure for application in DSSC and microelectromechanical systems (MEMS) energy harvesters. Analysis has been conducted using the simulation and fabrication method. The results show that the doping and the pore size of the substrate (Anodic Aluminum oxide membrane) largely affect the output voltage and current. The difference between the simulated and experimental results was less than 1%, which shows the accuracy of the simulation. Tuning of the band gap can be observed by the addition of Sr in the ZnO nanostructure. For microelectromechanical systems energy harvesters, Sr-doped ZnO nanostructures deposited on anodic aluminum oxide show 7.10 mV of voltage and 1.11 uA of current output. The addition of Sr doping in ZnO shows the improvement in the generated current and voltage for the energy harvester and the improvement in overall power conversion efficiency for dye-sensitized solar cells. MEMS-based energy harvesting devices and low-cost advanced solar cells are promising to improve the efficiency of energy generation at a small scale

Solar-Hybrid Cold Energy Storage System Coupled with Cooling Pads Backup: A Step towards Decentralized Storage of Perishables

Post-harvest loss is a serious issue to address challenge of food security. A solar-grid hybrid cold storage system was developed and designed for on-farm preservation of perishables. Computational Fluid Dynamic analysis was performed to assess airflow and temperature distribution inside the cold chamber. The system comprises a 21.84 m3 cubical cold storage unit with storage capacity of 2 tonnes. A hybrid solar system comprising 4.5 kWp PV system, 5 kW hybrid inverter, and 600 Ah battery bank was used to power the entire system. A vapor-compression refrigeration system (2 tonnes) was employed coupled with three cooling pads (filled with brine solution) as thermal backup to store cooling (−4 °C to 4 °C). Potatoes were stored at 8 °C for a period of three months (May 2019 to July 2019) and the system was tested on grid utility, solar, and hybrid modes. Solar irradiation was recorded in range of 5.0–6.0 kWh/(m2 × d) and average power peak was found to be 4.0 kW. Variable frequency drive was installed with compressor to eliminate the torque load and it resulted about 9.3 A AC current used by the system with 4.6 average Coefficient of Performance of refrigeration unit. The average energy consumed by system was found to be 15 kWh with a share of 4.3 kWh from grid and 10.5 kWh from solar, translating to 30% of power consumption from grid and 70% from solar PV modules. Overall, cold storage unit efficiently controlled total weight loss (7.64%) and preserved quality attributes (3.6 ⁰Brix Total soluble solids, 0.83% Titratable acidity, 6.32 PH) of the product during storage time

MAP/Microtubule Affinity Regulating Kinase 4 Inhibitory Potential of Irisin: A New Therapeutic Strategy to Combat Cancer and Alzheimer’s Disease

Irisin is a clinically significant protein playing a valuable role in regulating various diseases. Irisin attenuates synaptic and memory dysfunction, highlighting its importance in Alzheimer’s disease. On the other hand, Microtubule Affinity Regulating Kinase 4 (MARK4) is associated with various cancer types, uncontrolled neuronal migrations, and disrupted microtubule dynamics. In addition, MARK4 has been explored as a potential drug target for cancer and Alzheimer’s disease therapy. Here, we studied the binding and subsequent inhibition of MARK4 by irisin. Irisin binds to MARK4 with an admirable affinity (K = 0.8 × 107 M−1), subsequently inhibiting its activity (IC50 = 2.71 µm). In vitro studies were further validated by docking and simulations. Molecular docking revealed several hydrogen bonds between irisin and MARK4, including critical residues, Lys38, Val40, and Ser134. Furthermore, the molecular dynamic simulation showed that the binding of irisin resulted in enhanced stability of MARK4. This study provides a rationale to use irisin as a therapeutic agent to treat MARK4-associated diseases

On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction

Cancer is the second leading cause of mortality across the globe. Approximately 9.6 million people are estimated to have died due to cancer disease in 2019. Accurate and early prediction of cancer can assist healthcare professionals to devise timely therapeutic innervations to control sufferings and the risk of mortality. Generally, a machine learning (ML) based predictive system in healthcare uses data (genetic profile or clinical parameters) and learning algorithms to predict target values for cancer detection. However, optimization of predictive accuracy is an important endeavor for accurate decision making. Reject Option (RO) classifiers have been used to improve the predictive accuracy of classifiers for cancer like complex problems. In a gene profile all of the features are not important and should be shaved off. ML offers different techniques with their own methodology for feature selection (FS) and the classification results are dependent on the datasets each having its own distribution and features. Therefore, both FS methods and ML algorithms with RO need to be considered for robust classification. The main objective of this study is to optimize three parameters (learning algorithm, FS method and rejection rate) for robust cancer prediction rather than considering two traditional parameters (learning algorithm and rejection rate). The analysis of different FS methods (including t-Test, Las Vegas Filter (LVF), Relief, and Information Gain (IG)) and RO classifiers on different rejection thresholds is performed to investigate the robust predictability of cancer. The three cancer datasets (Colon cancer, Leukemia and Breast cancer) were reduced using different FS methods and each of them were used to analyze the predictability of cancer using different RO classifiers. The results reveal that for each dataset predictive accuracies of RO classifiers were different for different FS methods. The findings based on proposed scheme indicate that, the ML algorithms along with their dependence on suitable FS methods need to be taken into consideration for accurate prediction

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

Development of an imaging system for spatially real-time measurement of drying parameters in industrial drying units

Lack of accurate information for the drying kinetics is the main barrier to optimize drying processes effectively. Online monitoring of drying data is not common due to practical issues involved in set-up application. In this study an imaging system was developed for large drying units to investigate the quality changes (color kinetics and shrinkage) combine with weight loss of food product spatially and non-destructively during convective drying. The imaging setup was used in a diagonal-airflow batch dryer and shifted along the dryer length to take data (food images and weight loss) at different positions using potato slices (5mm thick, 60°C) as drying material. First order model fitted well to value of ∆L* and ∆b* while ∆a* and ∆E fitted in zero order model. The experimental and models predicted color kinetics revealed good correlation coefficients (R2 = 0.88-0.99, P ˂ 0.0001). The shrinkage of the samples exhibited two periods; a faster with MR up to X/Xo ≥ 0.3 and afterwards it became slow employing polynomial cubic regression (R2 = 0.97-0.99, P ˂ 0.0001). The imaging system effectively measured the change in quality parameters spatially along the dryer. The current study would help to develop and implement low cost setup for online measurements used for the assessment of drying processes in large drying units.