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This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

Protecting people from the risks associated with products is a critical concern in today’s economy. Pakistan, being the world’s fifth most populous country, lacks the framework of warning labels and therefore faces a significant gap in product warning labels. Pakistan is a representative of a number of countries that export a variety of products to Pakistan; however, warning labels on these goods are typically in English, which might mislead people of Pakistan in perceiving the hazard level. It is therefore imperative to conduct research into the non-textual and cross-cultural understanding of labels from the perspective of Pakistan. This study examined the applicability of ANSI Z535.4 in the context of Pakistan. A total of 66 (34 male and 32 female) undergraduate students with a mean age of 20.5 participated in this study. A meticulous experiment was designed using a nine-point rating scale with anchors on both sides, where one represented ‘not at all hazardous’ and nine represented ‘extremely hazardous’. Participants rated each component of warning labels, i.e., color, symbol, signal words, and their complex configurations. The results showed alignment with the ANSI Z535.4 standards for some components (i.e., colors, symbols, and signal words) and complex configurations, whereas no significant difference was found in perceived hazard levels between green (M = 3.167), blue (M = 3.591, and yellow (M = 3.652) colors, with a p-value greater than 0.05. Participants did not differentiate significantly between signal words, i.e., caution (M = 5.182) and warning (M = 5.879). Participants also did not differentiate significantly between complex configurations, i.e., safety alert–caution–yellow (M = 5.076) and safety alert–warning–orange (M = 5.197), with p-values greater than 0.05. These results state that discrepancies in the perception of warning labels exist. This study is the first of its kind conducted in the context of Pakistan, which will help policy makers to consider the findings before implementing a policy. In fact, differences in perception could result in failure to take appropriate precautions. Nonetheless, these nuances can be overcome with proper awareness through training for the people.

The proboscis extension response (PER) assay revealed the responsiveness of three subspecies of the honeybee Apis mellifera [A. m. jemenitica (AMJ), A. m. carnica (AMC), and A. m. ligustica (AML)] to water and different concentrations (0.00001, 0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, and 1.5 M) of three sugars (fructose, glucose, and sucrose) during the summer and fall seasons. The tested bee subspecies showed significantly different PERs to sugar types across the seasons. The water responsiveness of AMJ, a native bee subspecies, was significantly lower than that of AMC and AML, which showed an equally higher water response in both seasons. During the summer season, AMJ and AMC were equally responsive to each sugar type at all tested concentrations. AML was relatively less responsive to glucose at 0.001, 0.001, 0.01, 0.1, 0.5, and 1.0 M than to fructose and sucrose during the summer season. During the fall season, AMJ was equally responsive to glucose and sucrose at all tested concentrations but showed a significantly different response between fructose and sucrose at 0.001, 0.01, 0.1, 0.5, and 1.0 M concentrations. The PER of AMJ to fructose was lower than that of glucose and sucrose. AMC was equally responsive to all tested sugars at all concentrations, and AML showed a differential response between glucose and sucrose at different concentrations during the fall season. The inter-specific species comparisons revealed that all tested subspecies were equally responsive to fructose at all tested concentrations, and AMJ was more responsive to glucose and sucrose than AMC and AML during both seasons. AMC and AML showed no differences in PER to glucose and sucrose in either season. The AMJ, AMC, and AML nectar and pollen foragers showed no significant differences in PER to glucose and sucrose. The AMC nectar foragers were highly responsive to sucrose than pollen foragers at higher sucrose concentrations (0.1, 0.5, 1.0, and 1.5 M). The AML (nectar forager vs. pollen forgers) showed identical PER to sucrose and glucose but a higher response of nectar foragers to high glucose concentrations (0.5, 1.0, and 1.5 M) than pollen foragers. For water responsiveness, AMJ nectar and pollen foragers showed similar PER to water, whereas AMC and AML pollen foragers were significantly more responsive to water than nectar foragers.

The design of an experimental approach, the Box–Behnken design, was implemented to optimize the chromatographic condition to develop a rapid HPLC procedure for quantification of a ternary mixture of metoprolol (MET), telmisartan (TEL), and amlodipine (AML) from the formulation. The perturbation plots, contour, and 3D response surface pictures were developed to study the impact of each variable on the analytes’ retention time and the probable interaction between the parameters with fewer chromatographic runs. The optimized HPLC method separated the three analytes within 5 min with excellent selectivity and peak shape on a Zorbax C18 HPLC column using acetonitrile and phosphate buffer (20 mM, pH 5.8) with isocratic elution at a 1.1 mL/min flowrate. A wavelength 230 nm was utilized to monitor the elute. The validation of proposed method demonstrated a wide linearity range of 10–200 µg/mL for MET and TEL and 5–50 µg/mL for AML along with an excellent correlation coefficient. The correctness of the HPLC approach was further confirmed by excellent recovery of the added amount of analytes utilizing the standard addition technique. The recommended HPLC approach was employed safely for quality assurance of the formulation, because the evaluation of the method’s greenness and whiteness confirmed the environmentally friendly nature of the approach.

Challenges faced in network security have significantly steered the deployment timeline of Fifth Generation (5G) communication at a global level; therefore, research in Sixth Generation (6G) security analysis is profoundly necessitated. The prerogative of this paper is to present a survey on the emerging 6G cellular communication paradigm to highlight symmetry with legacy security concepts along with asymmetric innovative aspects such Artificial Intelligence (AI), Quantum Computing, Federated Learning, etc. We present a taxonomy of the threat model in 6G communication in five security legacy concepts, including Confidentiality, Integrity, Availability, Authentication and Access control (CIA3). We also suggest categorization of threat-countering techniques specific to 6G communication into three types: cryptographic methods, entity attributes and Intrusion Detection System (IDS). Thus, with this premise, we distributed the authentication techniques in eight types, including handover authentication, mutual authentication, physical layer authentication, deniable authentication, token-based authentication, certificate-based authentication, key agreement-based authentication and multi-factor authentication. We specifically suggested a series of future research directions at the conclusive edge of this survey.

In this article, we developed the idea of q-time scale calculus in quantum geometry. It includes the q-time scale integral operators and ∆q-differentials. It analyzes the fundamental principles which follow the calculus of q-time scales compared with the Leibnitz–Newton usual calculus and have few crucial consequences. The ∆q-differential reduced method of transformations was proposed to work out on partial Δq-differential equations in time scale. With easily computable coefficients, the result is calculated in the version of a power series which is convergent. The performance and effectiveness of the proposed procedure are also illustrated, and Matlab software is applied for calculation with the support of some fascinating examples. It changes when σt=t and q = 1; then, the solution merges with usual calculus for the mentioned initial value problem. The finding of the present work is that the Δq-differential transformation reduced method is convenient and efficient.

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques.

We surveyed and selectively sampled the major water sources in Bamyan city and the surrounding area to assess the water quality. Water quality measurements were taken in situ and more samples were collected for laboratory analysis from canals, rivers, springs, wells, and water supply systems. In urban areas, water supply systems provide 36% of the drinking water, but in rural areas, this source accounts for only 7% of drinking water supplies. Wells comprise 33% and 15% of urban and rural water supplies, respectively, while canals and rivers are modest water sources for Bamyan communities. Basic water quality parameters, such as pH, EC, and TDS, were variable with high values in some areas. Most of the samples fall in the range of potable water, but some had a high TDS and EC indicating that there is the potential of contamination. Values of pH were mostly were mostly in the range of drinking water (6.5–9.5). A Drinking Water Quality Index (DWQI) was calculated to better understand the water quality issues for the potable water supplies. Subsets of representative samples were analyzed for 17 selected chemical elements and other constituents. Barium (Ba) was detected in almost all of the water samples, while arsenic (As) was detected in about 9% of the analyzed samples, and this was mostly associated with thermal springs. Concentrations of Mn and Cu in some samples exceeded that of the water quality standards, while Zn concentrations were below tolerable limits in all of the samples. Most of the analyzed water samples were hard, and several samples showed evidence of microbial pollution in urban areas. Rivers originating from snow and glacier melting had excellent quality for drinking.

In Pakistan, Apis cerana, the Asian honeybee, has been used for honey production and pollination services. However, its genomic makeup and phylogenetic relationship with those in other countries are still unknown. We collected A. cerana samples from the main cerana-keeping region in Pakistan and performed whole genome sequencing. A total of 28 Gb of Illumina shotgun reads were generated, which were used to assemble the genome. The obtained genome assembly had a total length of 214 Mb, with a GC content of 32.77%. The assembly had a scaffold N50 of 2.85 Mb and a BUSCO completeness score of 99%, suggesting a remarkably complete genome sequence for A. cerana in Pakistan. A MAKER pipeline was employed to annotate the genome sequence, and a total of 11,864 protein-coding genes were identified. Of them, 6750 genes were assigned at least one GO term, and 8813 genes were annotated with at least one protein domain. Genome-scale phylogeny analysis indicated an unexpectedly close relationship between A. cerana in Pakistan and those in China, suggesting a potential human introduction of the species between the two countries. Our results will facilitate the genetic improvement and conservation of A. cerana in Pakistan.

The present study aims to determine the neuroprotective effect of Bergenin against spatial memory deficit associated with neurodegeneration. Preliminarily, the protective effect of Bergenin was observed against H2O2-induced oxidative stress in HT-22 and PC-12 cells. Further studies were performed in 5xFAD Tg mouse model by administering Bergenin (1, 30 and 60 mg/kg; orally), whereas Bergenin (60 mg/kg) significantly attenuated the memory deficit observed in the Y-maze and Morris water maze (MWM) test. Fourier transform-infrared (FT-IR) spectroscopy displayed restoration of lipids, proteins and their derivatives compared to the 5xFAD Tg mice group. The differential scanning calorimeter (DSC) suggested an absence of amyloid beta (Aβ) aggregation in Bergenin-treated mice. The immunohistochemistry (IHC) analysis suggested the neuroprotective effect of Bergenin by increasing Reelin signaling (Reelin/Dab-1) and attenuated Aβ (1–42) aggregation in hippocampal regions of mouse brains. Furthermore, IHC and western blot results suggested antioxidant (Keap-1/Nrf-2/HO-1), anti-inflammatory (TLR-4/NF-kB) and anti-apoptotic (Bcl-2/Bax/Caspase-3) effect of Bergenin. Moreover, a decrease in Annexin V/PI-stained hippocampal cells suggested its effect against neurodegeneration. The histopathological changes were reversed significantly by Bergenin. In addition, a remarkable increase in antioxidant level with suppression of pro-inflammatory cytokines, oxidative stress and nitric oxide production were observed in specific regions of the mouse brains.

Thermal springs are natural hydrogeological features which are highly affected by local volcanism or tectonic activity. Thermal springs are the best source of hydrothermal energy to heat houses and aid in the recovery of skin diseases. However, they consist of some heavy and trace metals such as arsenic, lead, zinc, copper, iron, and so forth. Somehow, the thermal springs of central Bamyan have become contaminated with some essential trace elements. Thus, this study was conducted to assess and determine the number of these trace elements in the thermal springs. To achieve these objectives, a preliminary survey, water sampling, and in situ measurements of physicochemical parameters were conducted in research areas. All the collected water samples were analyzed chemically to determine the amount of trace elements, including arsenic, barium, copper, iron, manganese, and zinc. The study shows that temperature ranged from 16 to 32 °C, while the average pH value was 6.25. Almost all of the trace elements showed an extremely high value of electrical conductivity (average: 5713 µS/cm) and significantly high total dissolved solids (average: 3063 mg/L). The average value of chloride was 797 mg/L, which is dramatically higher than standard values. In regard to trace element concentration, almost all thermal springs were heavily contaminated with arsenic and it was recorded as 100 µg/L in the eastern part of central Bamyan. The average amounts of barium, copper, iron, manganese, and zinc were 4.14, 6.05, 1.90, 1.76, and 0.74 mg/L, respectively. In conclusion, the water of the thermal springs of central Bamyan are not suitable for human consumption because of the significant amount of trace elements as well as the high-water quality index value. Using these springs for drinking and irrigation purposes has been deemed inappropriate.

Kabul is one of the most populated cities in Afghanistan and providing resources to support this population in an arid climate presents a serious environmental challenge. The current study evaluated the quality of local Kabul Basin groundwater to determine its suitability water for drinking and irrigation purposes now and into the future. This aim was aided through groundwater parameter assessment as well as determination of Water Quality Index (WQI) developed from 15 observation points near the city. The results of our physicochemical analysis illustrate that groundwater in the majority of areas of the Kabul Basin is not generally suitable for human consumption, and in some cases the concentrations of many contaminants are higher than accepted health standards or water quality benchmarks. The aquifer underlies an arid landscape, and because of this 85% of the samples tested are very hard while just over 13% are classified as hard. Groundwater in the Kabul Basin is typically high in calcium and magnesium and overall classified as a calcium bicarbonate water type. Overall, more than 60% of the analyzed samples had concentrations higher than the World Health Organization (WHO) standard of total dissolved solids (TDS), 10% in total hardness (TH), about 30% in turbidity and more than 90% in magnesium. The results show that based on WQI, without treatment, roughly 5% of groundwater in the studied area is unsuitable for human consumption, while 13.3% is very poor and 40% is poor quality water. Approximately 40% of the assessed groundwater has good quality and could be used as drinking water for future development. Groundwater in some areas shows evidence of pollution and high dissolved solids content, rendering these sources unsuitable for either drinking or irrigation purposes.

The production and up-regulation of inflammatory mediators are contributing factors for the development and maintenance of neuropathic pain. In the present study, the post-treatment of synthetic 1,3,4 oxadiazole derivative (B3) for its neuroprotective potential in chronic constriction injury-induced neuropathic pain was applied. In-silico studies were carried out through Auto Dock, PyRx, and DSV to obtain the possible binding and interactions of the ligands (B3) with COX-2, IL-6, and iNOS. The sciatic nerve of the anesthetized rat was constricted with sutures 3/0. Treatment with 1,3,4-oxadiazole derivative was started a day after surgery and continued until the 14th day. All behavioral studies were executed on day 0, 3rd, 7th, 10th, and 14th. The sciatic nerve and spinal cord were collected for further molecular analysis. The interactions in the form of hydrogen bonding stabilizes the ligand target complex. B3 showed three hydrogen bonds with IL-6. B3, in addition to correcting paw posture/deformation induced by CCI, attenuates hyperalgesia (p < 0.001) and allodynia (p < 0.001). B3 significantly raised the level of GST and GSH in both the sciatic nerve and spinal cord and reduced the LPO and iNOS (p < 0.001). B3 attenuates the pathological changes induced by nerve injury, which was confirmed by H&E staining and IHC examination. B3 down-regulates the over-expression of the inflammatory mediator IL-6 and hence provides neuroprotective effects in CCI-induced pain. The results demonstrate that B3 possess anti-nociceptive and anti-hyperalgesic effects and thus minimizes pain perception and inflammation. The possible underlying mechanism for the neuroprotective effect of B3 probably may be mediated through IL-6.

Background and Objective: Medication non-adherence is a preventable reason for treatment failure, poor blood pressure control among hypertensive patients and the geriatric population owing to poor physical activity is more vulnerable strata. The objective of this study is to investigate medication adherence and its associated factors among Pakistani geriatric hypertensive patients. Methods: A cross-sectional survey-based study was conducted at the out-patient department of the cardiac center from May 2018 to August 2018. A universal sampling technique was used to approach patients and 262 eligible consented patients were interviewed to collect information about socio-demographics, health, and disease-related characteristics using a structured questionnaire. The Morisky Levine Green test was used for the assessment of medication adherence. The Barthel index and single item literacy screener (SILS) was used to measure performance in activities of daily living and health literacy respectively. Chi-square tests and multivariate binary logistic regression analysis were performed to find factors by using SPSS version 20. Results: Of the total 262 participants, about 38.9% (n = 102) were scored 4 and considered adherent while 61.1% (n = 160) were considered as non-adherent. In logistic regression analysis, self-reported moderate (OR = 3.538, p = 0.009) and good subjective health (OR = 4.249, p = 0.008), adequate health literacy (OR = 3.369, p < 0.001) and independence in performing activities of daily living (OR = 2.968, p = 0.002) were found to be independent predictors of medication adherence among older hypertensive patients. Conclusion: Medication adherence among the older hypertensive population in Pakistan is alarmingly low. This clearly requires patient-centered interventions to overcome barriers and educating them about the importance of adherence.

Nitrogen-doped graphene oxide sheets (N-GOs) are prepared by employing N-containing polymers such as polypyrrole, polyaniline, and copolymer (polypyrrole-polyaniline) doped with acids such as HCl, H2SO4, and C6H5-SO3-K, which are activated using different concentrations of KOH and carbonized at 650 °C; characterized using SEM, TEM, BET, TGA-DSC, XRD, and XPS; and employed for the removal of environmental pollutant CO2. The porosity of the N-GOs obtained were found to be in the range 1–3.5 nm when the KOH employed was in the ratio of 1:4, and the XRD confirmed the formation of the layered like structure. However, when the KOH employed was in the ratio of 1:2, the pore diameter was found to be in the range of 50–200 nm. The SEM and TEM analysis reveal the porosity and sheet-like structure of the products obtained. The nitrogen-doped graphene oxide sheets (N-GOs) prepared by employing polypyrrole doped with C6H5-SO3-K were found to possess a high surface area of 2870 m2/g. The N-GOs displayed excellent CO2 capture property with the N-GOs; PPy/Ar-1 displayed ~1.36 mmol/g. The precursor employed, the dopant used, and the activation process were found to affect the adsorption property of the N-GOs obtained. The preparation procedure is simple and favourable for the synthesis of N-GOs for their application as adsorbents in greenhouse gas removal and capture.