Long term effects of childhood trauma and abuse: Narrative on functional neurological disorder.

Functional neurological disorder is a condition in which a person experiences physical symptoms that cannot be fully explained by a medical condition. In Pakistan, domestic violence as well as emotional, physical and sexual abuse in children are prevalent. Despite legal and social support for victims, stigmatisation regarding seeking psychological help complicates the challenge. Some of the research culminated that patients with neurological disorder reported high level of sexual abuse and trauma. The symptomatology of functional neurological disorder is being ignored in Asian countries due to indigenous factors like poverty, lack of information on reporting abuse, poor law-enforcement and victim blaming. Functional neurological disorder can be manifested in various ways in the human body, such as blindness, paralysis, dystonia, swallowing difficulties, difficulty walking, motor symptoms affecting limbs, voice production, problems in sensory functions, problems in cognitive function, psychogenic non-epileptic seizures and even dementia, whereas differential diagnosis is established after screening for organicity. The common risk factors of functional neurological disorder may include psychosocial stressors, family history of functional neurological disorder, and significant life changes. It is critical to understand the disorder in reference to predisposing risk factors, cultural context, comorbidities and gender specification to diagnose and treat functional neurological disorder in time so that better intervention protocols could be devised to treat it efficiently.

Numerical simulation of lead-free vacancy ordered Cs2PtI6 based perovskite solar cell using SCAPS-1D.

In recent years, vacancy-ordered halide double perovskites have emerged as promising non-toxic and stable alternatives for their lead-based counterparts in optoelectronic applications. In particular, vacancy ordered Cs2PtI6 has emerged as a star material because of its high absorption coefficient, band gap of 1.37 eV, and long minority carrier lifetime. Despite substantial experimental research on this new class of material, theoretical simulations of their device properties remain scarce. In this work, a novel n-i-p device architecture (FTO/SnO2/Cs2PtI6/MoO3/C) is theoretically investigated using a solar cell capacitance simulator (SCAPS-1D). Theoretical investigations are carried out in order to optimize the device performance structure by varying the perovskite and selective charge transport layer thickness, absorber and interface defect density, operating temperature, back contact, series and shunt resistance, respectively. The optimized device showed an impressive power conversion efficiency (PCE) of 23.52% at 300 K, which is higher than the previously reported values. Subsequent analysis of the device's spectral response indicated that it possessed 98.9% quantum efficiency (QE) and was visibly active. These findings will provide theoretical guidelines for enhancing the performance of Cs2PtI6-based photovoltaic solar cells (PSCs) and pave the way for the widespread implementation of environmentally benign and stable perovskites.

Recent progress in use of MXene in perovskite solar cells: for interfacial modification, work-function tuning and additive engineering.

The use of perovskites in photovoltaic and related industries has achieved tremendous success over the last decade. However, there are still obstacles to overcome in terms of boosting their performance and resolving stability issues for future commercialization. The introduction of a new 2D material of halide perovskites is now the key advancement in boosting the solar energy conversion efficiency. The implication of a new 2D material (MXene) in perovskite solar cells has been initiated since its first report in 2018, showing excellent transparency, electrical conductivity, carrier mobility, superior mechanical strength, and tunable work function. Based on distinctive features at the hetero-interface, halide perovskite and MXene heterostructures (HPs/Mx) have recently exhibited exceptional improvements in both the performance and stability of perovskite solar cells. Furthermore, the wide families of HPs and MXene materials allow playing with the composition and functionalities of HP/Mx interfaces by applying rational designing and alterations. In this review a comprehensive study of implementing MXenes in perovskite solar cells is presented. First, the implementation of MXenes in perovskites as an additive, and then in charge extraction layers (HTL/ETL), is described in detail. It is worth noting that still only Ti3C2Tx, Nb2CTx,V2CTx MXene is being incorporated into perovskite photovoltaics. Finally, the present obstacles in the use of MXenes in PSCS are discussed, along with the future research potential. This review is expected to provide a complete and in-depth description of the current state of research and to open up new opportunities for the study of other MXenes in PSCs.

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies.

The purpose of this research was to provide a "systematic literature review" of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches-to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

Fluorescence modulation of cadmium sulfide quantum dots by azobenzene photochromic switches.

We have investigated the attachment of azobenzene photochromic switches on the modified surface of cadmium sulfide (CdS) quantum dots (QDs). The modification of CdS QDs is done by varying the concentration of the capping agent (mercaptoacetic acid) and NH3 in order to control the size of the QDs. The X-ray diffraction studies revealed that the crystallite size of CdS QDs ranged from 6 to 10 nm. The azobenzene photochromic derivatives bis(4-hydroxybenzene-1-azo)4,4'(1,1' diphenylmethane) (I) and 4,4'-diazenyldibenzoic acid (II) were synthesized and attached with surface-modified CdS QDs to make fluorophore-photochrome CdS-(I) and CdS-(II) dyad assemblies. Upon UV irradiation, the photochromic compounds (I) and (II) undergo a reversible trans-cis isomerization. The photo-induced trans-cis transformation helps to transfer photo-excited electrons from the conduction band of the CdS QDs to the lowest unoccupied molecular orbital of cis isomer of photochromic compounds (I) and (II). As a result, the fluorescence of CdS-(I) and CdS-(II) dyads is suppressed approximately five times compared to bare CdS QDs. The fluorescence modulation in such systems could help to design luminescent probes for bioimaging applications.