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Abstract The Upper Indus Basin (UIB) of Pakistan is home to three largest mountain ranges: Karakoram, Hindukush, and the Himalayas. There’s a hovering danger of reduced water resources due to climate-induced warming, since the Indus Basin relies mostly on snow and glacier melt runoff from these mountains. In this study, the hydrology of these areas is studied to estimate variability in water resources under changing climate. A coupled hydrologic/hydraulic model, MIKE SHE/MIKE HYDRO RIVER, was used to calibrate and validate the model using streamflow data at the Shatial gauge station. The bias-corrected multi-model ensemble dataset accessed from the coordinated regional climate downscaling experiment (CORDEX) for two scenarios RCP 4.5 and RCP 8.5 were used up to the end of twenty-first century for the projection of stream flow. The calibrated model depicts an increase of 86% for RCP4.5 and 97% for RCP8.5, dominated by increased snow melt contribution due to consistent warming trends (average increase of 2.3 and 4 °C, annually for RCP 4.5 and 8.5, respectively). The most prominent increase was observed in winter months when predicted flows increased by 300% from historic. With predicted annual average available water of 4500 m3/s under RCP8.5, the study concludes that the available water would relieve some of the water stress in the country. However, the increased availability of water can also cause catastrophic floods. More flood defense and storage structures are needed to improve management practices in the downstream areas, particularly during wet and dry years.

Abstract Background Agriculture, one of the most hazardous occupations globally, accounts for a significant proportion of work-related injuries, particularly in developing countries like India. However, lack of awareness and possibly low education levels make farmers in the unorganized sector vulnerable to eye injuries. This study aimed to identify the barriers to and facilitators of protective eyewear among farmers. Methods A multicentric study was conducted to assess occupational ocular hazards and preventive strategies among farmers in southern India. The facilitating factors and barriers to spectacles or protective eyewear among the study population were explored using a snapshot qualitative study conducted from two centres in Tamil Nadu and one in Karnataka. Thematic analysis was used to analyse the facilitators of and barriers to protective eyewear among farmers. Results Five focus group discussions were conducted with 31 farmers (mean age 55.8 ± 9.2 years) from three centers. Six themes were identified: occupational hazards while farming, practice patterns for managing occupational hazards, uses of protective eyewear while farming, benefits and challenges of protective eyewear, perceptions about protective eyewear and suggestions for improvement. Eye injuries from dust, branches, and chemicals were common, and farmers relied on home remedies for minor issues. Protective eyewear was appreciated for preventing injuries and improving safety but faced challenges such as discomfort, poor fit, and aesthetic concerns, particularly among women. Conclusions Although there is awareness regarding the need for protective eyewear, it is often not used due to discomfort during work or concerns about possible breakage of the spectacles. It is imperative for primary eye care professionals to raise awareness regarding the importance of ocular protection in the workplace.

Abstract Yersinia pestis, a Gram-negative bacterium is the causative agent of the fatal communicable disease plague. The disease had a profound impact on human history. Plague bacteria are usually transmitted to humans through the bite of an infected rat flea. Earlier studies have indicated that Y. pestis can survive in environmental matrices e.g. water and soil. This study aimed to generate a peptide-based screen for identification of Y. pestis particularly from environmental matrices. We employed a shotgun proteomic approach using nano-liquid chromatography-tandem mass spectrometry (nLC-MS/MS) to discover Y. pestis-specific peptides. The pure cultures of Y. pestis and related species were grown, their proteome were delineated and analyzed by in silico tools to discover 61 Y. pestis specific peptides. Additionally, 148 peptides were discovered from proteins of Y. pestis-specific plasmids and chromosomal-associated virulence markers. To validate this screen of 209 peptides, various concentrations of Y. pestis (ranging from 1.3 × 108 to 1.3 × 105 cfu) were spiked into garden soil. Y. pestis could be identified in all samples except un-spiked negative control soil sample. This study offers a valuable method for the identification of Y. pestis, by tandem mass spectrometry which may be used in environmental and clinical matrices.

Abstract Naegleria fowleri, the causative agent of Primary Amoebic Meningoencephalitis (PAM), is commonly found in warm freshwater environments and can enter the brain through nasal passages during activities like swimming or ablution. PAM has a high fatality rate, raising concerns about its global health impact. In Pakistan, particularly in Karachi, a significant number of cases have been reported, often with no history of recreational water exposure, but with regular ablution using tap water. This study analyzed the physicochemical parameters, abundance of total and fecal coliforms, and detected N. fowleri and other Naegleria species in tap water samples from Karachi using PCR with ITS- and Naegl-primers. Almost all samples exhibited high temperatures, low chlorine levels, and a high presence of coliforms. N. fowleri and other Naegleria species were detected in 11 out of 39 samples. Sequence analysis identified N. fowleri in tap water from the Golimar and Lyari areas of Karachi, while the other nine samples revealed different Naegleria species. This study suggests that the combination of high temperatures, insufficient chlorination, and the presence of coliforms may create favorable conditions for N. fowleri growth. However, these factors are not exclusive to the Golimar and Lyari areas, indicating that other environmental or infrastructural factors, not detailed in this study, may have contributed to the presence of N. fowleri in that specific location.

Post-disaster functionality of the potable water supply infrastructure is crucial for maintaining public health and safety. The failure of the water supply system may harm inhabitants and significant users, which can impact other services and various types of infrastructures. It is vital to assess climate change impacts on water resources to ensure resilience and sustainable management. The study assessed the resilience of 22 municipal water schemes using an indicator-based framework, How Tough is Wash (HTIW), across six domains: environment, infrastructure, community governance, water supply management, supply chains, and institutional support. The sites of municipal water supply were purposively selected based on specific criteria to include those water supply schemes which have been directly affected by flooding or are vulnerable to the impact of the flood. The resilience scores ranged from (6 to 7.5) for schemes with very low resilience, (7.5 to 10.5) for those classified as poor, and (10.6 to 16.5) for schemes considered moderately resilient. It was discovered that protective measures against the risk of flood were negligible. Community engagement governance and water supply management needed to be stronger. Local governments can pinpoint current advantages and disadvantages by utilizing these metrics and their impact on the public water supply's resilience and strategically allocate resources for optimal outcomes. Furthermore, the metrics that have been suggested simplify and unify the multiple dimensions of the elements, enabling decision-makers to optimize the measures.

Sintering of tungsten nominally requires several hours at ultrahigh temperatures. We show this refractory metal can be sintered quickly by direct injection of current into dog bone shaped specimens. The current rate was varied from 10 A s−1 (fast) to 0.1 A s−1 (slow), leading to sintering in 2–200 s, respectively. Sintering occurred at the same current density, regardless of the current rate. In all instances, the samples sintered when they reached 1000°C. The phenomenological behavior of flash sintering of metals is described by three stages: an incubation time followed by electroluminescence, and finally by abrupt sintering to full density. It is conjectured that rapid sintering is instigated by the formation of Frenkel pairs (vacancies and interstitials), as well as electrons and holes. The point defects accelerate mass transport, whereas electrons and holes recombine to form photons. Calorimetric measurements show an endothermic reaction attributed to the creation of defects. Estimates suggest an unusually large concentration of Frenkel pairs. PS: Flash sintering is different than electro‐discharge‐sintering where a capacitor is discharged in a few milliseconds to sinter a metal. Here, instead of dumping large amount of energy at once, a power supply is programed to control the rate of current injection. [ABSTRACT FROM AUTHOR]

The solar vortex engine (SVE) aims to replace the tall and expensive chimney structure in solar updraft power plants by a shorter less expensive structure named the vortex generator. In this study, the entire SVE system is simulated by CFD to determine the appropriate location for the turbine unit as well as prove the capability of the vortex generator in replacing the chimney. Three different cases for the SVE are considered and compared to corresponding cases of the solar chimney power plant (SCPP). Results revealed that the optimum turbine location is at the outlet hole of the vortex generator. An updraft air velocity of 1.82 m/s was achieved at the outlet hole, compared to 1.56 m/s at the base of a solar chimney with the same diameter as the upper hole. The consideration of the turbine pressure drop did not affect the formation and preservation of the air vortex. So, the 1 m high vortex generator successfully replaced the 8.6 m chimney component in solar updraft power plants, greatly reducing the cost and construction complexity of the plant. The vortex generator accelerated the air delivered from the solar collector, increasing its velocity by 14 times. The SVE's power output is directly proportional to the static pressure drop across the turbine. The mean difference values along the air vortex field between the cases with and without a turbine are 0.67 Pa and 0.026 m/s for the static pressure drop and velocity magnitude, respectively. [ABSTRACT FROM AUTHOR]

Abstract The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO’s resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis.

Abstract Wildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

Abstract The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

Abstract Background Post‐traumatic stress disorder (PTSD) is a mental health disorder resulting from exposure to traumatic events, manifesting in various debilitating symptoms. Despite available treatments, many individuals experience inadequate response or significant side effects. Previous reviews suggest promising outcomes with MDMA‐assisted psychotherapy (MDMA‐AT), but limitations prompt the need for a comprehensive evaluation. Methods We searched various online databases and registries such as MEDLINE (via PubMed), Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov to retrieve RCTs that fit our inclusion criteria. We performed meta‐analyses using Review Manager by applying a random‐effects model. Dichotomous and continuous outcomes were pooled as risk ratios (RR) and standard mean difference (SMD), respectively. Results Nine studies with a total of 297 participants with PTSD were included in our meta‐analysis. The control group consisted of inactive doses of MDMA (25–40 mg) or placebo. Our meta‐analysis showed that MDMA‐AT led to a significant reduction in the Clinician‐Administered PTSD Scale for DSM‐5 (CAPS‐5) severity scores as compared to the control group (SMD −1.10, 95% CI: −1.62 to −0.59). More patients in the MDMA‐AT group exhibited significant response (RR 1.59, 95% CI: 1.22, 2.08) and remission (RR 2.32, 95% CI: 1.47 to 3.66) as compared to patients in the control group. There was no significant difference regarding the incidence of ≥1 treatment‐emergent adverse events (TEAE), ≥1 severe TEAE, and suicidal ideation between the two groups. Conclusion MDMA‐AT demonstrates significant efficacy in improving PTSD symptoms, enhancing both response and remission rates in individuals with chronic, treatment‐resistant PTSD, while maintaining a favorable safety profile.