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This paper adds to the existing body of knowledge by incorporating the role of fiscal decentralization (FD) in influencing CO 2 emissions. Therefore, this study looked at the effect of FD on CO 2 emissions in the presence of nonrenewable energy consumption (NRE), renewable energy consumption (REN), gross domestic product (GDP), and trade openness (TOP) for the period 1994–2018 in Japan. Thus, the current work intends to fill this knowledge gap by employing econometric techniques such as Bayer and Hanck cointegration, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR). Additionally, the frequency domain causality analysis is used in the investigation to determine the causal impact of FD, NRE, REN, GDP, and TOP on CO2 emissions. The novelty of the frequency-domain approach is that it can differentiate between nonlinearity and causality levels and show causality among parameters with different frequencies. The DOLS, FMOLS, and CCR results reveal that NRE, GDP, and TOP augment CO 2 emissions in Japan, whereas FD and REN increase the quality of the atmosphere. Furthermore, the frequency causality test results show that FD, REN, GDP, and TOP have implications for CO 2 emissions in the long run, while NRE raises CO 2 emissions in the medium run. As a policy direction, the current study suggests expanding renewable energy consumption in Japan by emphasizing more on Sustainable Development Goals (7, 8, and 13).

This paper offers a new indulgence to the present literature by integrating the role of fiscal decentralization (FD) in affecting ecological footprint (EF). So, this study considered the effect of FD on EF in the existence of energy consumption (EC), technological innovation (TI), gross domestic product (GDP), and trade openness (TOP) from 1990 to 2018 in Pakistan. We employ econometric methods like Bayer & Hanck cointegration, fully modified ordinary least squares, dynamic ordinary least squares, and canonical cointegration regression for empirical analysis. Moreover, the frequency domain causality test is used to conclude the causal impact of FD, EC, TI, GDP, and TOP on EF. The regression results disclose that EC, GDP, and TOP boost EF in Pakistan; however, FD and TI promote the sustainability of the environment by reducing EF. Besides, the frequency causality outcomes indicate that FD, EC, TI, GDP, and TOP have insinuations for EF in the long term. As a policy recommendation, this research suggests that Pakistan could successfully integrate strategies to increase ecological quality by allowing the lower level of government to utilize eco-friendly technological innovations.

This novel research looked into the role of public-private partnership investment in energy in affecting Pakistan’s long-term environmental sustainability. Employing time series data from 1992 to 2018 and utilizing the autoregressive distributive lag model (ARDL) model, we found a long-term equilibrium association of ecological footprint with public-private partnership investment in energy, technological innovation, economic growth, and trade openness. Our outcomes showed a significant positive association between public-private partnership investment in energy and ecological footprint in the long-run and the short-run, specifying that the increase in public-private partnership investment in energy affects the environmental sustainability of Pakistan. Similarly, our study confirmed that technological innovation, economic growth, and trade openness increase the ecological footprint in Pakistan. It demonstrates that these factors are unfavorable to the sustainable environment in Pakistan. Furthermore, robustness check findings are analogous to the results of ARDL estimates, utilizing dynamic ordinary least squares and fully modified ordinary least squares. On the basis of the research conclusions, a multi-pronged sustainable development goal (SDG) model was proposed that addresses SDG 8 and SDG 13 while incorporating SDG 17 as a medium.

Non-technical losses (NTL) in power distribution, such as illegal meter tapping, cause significant financial losses for utilities, amounting to billions annually. This study evaluates various machine learning methods for NTL detection, addressing the challenge of imbalanced electricity consumption data. Seven techniques for data balancing were employed: Adaptive Synthetic Sampling (ADASYN), Random Over Sampling, Random Under Sampling, Near Miss Under Sampling, and several variations of Synthetic Minority Over Sampling (SMOTE), including Borderline-SMOTE, SMOTE-ENN, and SMOTE-Tomek links. The model comprises two stages: first, seven classification algorithms (Decision Tree, Logistic Regression, XGBoost, Random Forest, SVM, Naïve Bayes, and KNN) were tested across diverse training-testing ratios to identify optimal performance. The second stage applied the comprehensive consumption dataset along with data balancing techniques to improve algorithm efficacy. Performance metrics—accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (MCC)—were utilized for evaluation. Results revealed that the Random Forest algorithm, when paired with Random Over Sampling at a 70 − 30% training-testing ratio, yielded the highest metrics: 98.03% accuracy, 99.02% precision, surpassing existing literature. The model achieved exceptional precision (0.990) and the highest overall performance, with rigorous statistical testing confirming all improvements were significant at the 95% confidence level.

In today’s construction industry, there is a growing focus on sustainable concrete practices that involve utilizing various waste materials to enhance concrete’s mechanical properties. Coir fiber (CF), derived from coconut husk and generally a waste material, stands as a promising fiber addition to concrete in this regard. This study has investigated the fresh and mechanical properties of self-compacting concrete (SCC) reinforced with CF. Dosage rates were 0%, 0.1%, 0.2%, and 0.3% of the total concrete volume with a definite CF length of 19-20 mm. To assess the suitability of SCC mixes, evaluations were carried out using slump flow, V funnel, L Box, and J-ring tests, and the resulting fresh properties were compared to the criteria outlined in the EFNARC Guidelines. Superplasticizer (SP) dosage depended on the desired results of fresh SCC properties. Additionally, compressive strength was evaluated after 28 days of curing the SCC specimens. The results indicate that the compressive strength increases with fiber content up to 0.2%, but excessive SP dosage is required at 0.3% CF dosage and shows an inverse effect on compressive strength. The highest compressive strength was achieved in the SCC mix having 0.2% CF, which was 8.9% greater than the control mix.

In recent years, several non-specialist mediated interventions have been developed and tested to address problematic symptoms associated with autism. These can be implemented with a fraction of cost required for specialist delivered interventions. This review represents a robust evidence of clinical effectiveness of these interventions in improving the social, motor and communication deficits among children with autism. An electronic search was conducted in eight academic databases from their inception to 31st December 2018. A total of 31 randomized controlled trials were published post-2010 while only 2 were published prior to it. Outcomes pertaining to communication, social skills and caregiver-child relationship were meta-analyzed when reported in > 2 studies. A significant improvement was noted in child distress (SMD = 0.55), communication (SMD = 0.23), expressive language (SMD = 0.47), joint engagement (SMD = 0.63), motor skills (SMD = 0.25), parental distress (SMD = 0.33) parental self-efficacy (SMD = 0.42) parent-child relationship (SMD = 0.67) repetitive behaviors (SMD = 0.33), self-regulation (SMD = 0.54), social skills (SMD = 0.53) symptom severity (SMD = 0.44) and visual reception (SMD = 0.29). Non-specialist mediated interventions for autism spectrum disorder demonstrate effectiveness across a range of outcomes for children with autism and their caregivers.

A healthy road network plays a significant role in the socio-economic development of any country. Road management authorities struggle with pavement repair approaches and the finances to keep the existing road network to its best functionality. It has been observed that real-time road condition monitoring can drastically reduce road and vehicle maintenance expenses. There are various methods to analyze road health, but most are either expensive, costly, time-consuming, labor-intensive, or imprecise. This study aims to design a low-cost smart road health monitoring system to identify the road section for maintenance. An automized sensor-based system is developed to assist the road sections for repair and rehabilitation. The proposed system is mounted in a vehicle and the data have been collected for a more than 1000 km road network. The data have been processed using SPSS, and it shows that the proposed system is adequate for detecting the road quality. It is concluded that the proposed system can identify the vulnerable sections to add to the pavement maintenance plan. In the future, the created application can be launched as a smart citizen app where each car driver can install this application and can monitor the road quality automatically.

The blue swimming crab species Portunus pelagicus and Portunus segnis represent crucial components of commercial fisheries in the Northern Arabian Sea (NAS). This study employs partial coding regions of the cytochrome oxidase subunit 1 (COI) gene for DNA barcoding, with accession numbers OL840323 and OL840324 assigned to Portunus pelagicus and Portunus segnis, respectively, and deposited in the GenBank database. Analysis revealed high haplotype diversity and low nucleotide diversity within the populations of Portunus spp. Neutrality tests, specifically Tajima's D and Fu's F, yielded non-significant results. However, mismatch analysis indicated a potential population expansion event in the Arabian Sea. Evolutionary analyses were conducted comparing 21 sequences of Portunus spp. from GenBank, including 2 from Pakistan and 19 from various other regions, based on COI variation. Results from the analysis of molecular variance (AMOVA) suggested significant phylogeographic structuring (P < 0.05). The study highlights the efficiency of DNA barcoding in species identification, particularly in delineating cryptic varieties. Additionally, seasonal variations in the concentrations of ten trace elements, in carapace meat samples from 210 blue swimming crabs (Portunus pelagicus and Portunus segnis) collected from two locations in Karachi, Pakistan: West Wharf Fish Harbor (n = 100) and Korangi Creek (n = 110). Data collected during monsoon including zinc (Zn), iron (Fe), copper (Cu), cobalt (Co), chromium (Cr) as essential trace elements revealed the following order of essential trace elements: Fe > Zn > Cu > Co > Cr. In contrast, toxic trace elements such as aluminum (Al), lead (Pb), mercury (Hg), arsenic (As), and cadmium (Cd) were found in the order: Pb > Cd > Al > As > Hg and in non-monsoon period essential and toxic elements were found in the order: Fe>Zn>Cu>Cr>Co and As>Pb>Cd>Al>Hg. Metal concentrations were assessed using Atomic Absorption Spectroscopy (AAS), which was chosen for its sensitivity and accuracy in quantifying trace elements. The presence of elevated levels of essential trace elements (Fe, Zn, and Cu) in the aquatic environment is attributed to industrial and maritime activities in the Arabian Sea. Understanding the dynamics of these populations and their trace element uptake is significant for conservation, fishery management, and public health.

The increasing levels of heavy metals in aquatic environments, driven by human activities, pose a critical threat to ecosystems’ overall health and sustainability. This study investigates the bioaccumulation of heavy metals (Pb, Cu, Cr, and Cd) in water, sediment, and three fish species ( Catla catla , Labeo rohita , Cirrhinus mrigala ) of different feeding zones within Chashma Barrage, located in the Mianwali district of Punjab, Pakistan, on the Indus River. A comprehensive analysis, including an assessment of associated human health risks, was conducted. Thirty samples from all three sites for each fish species, with an average body weight of 160 ± 32 g, were collected from Chashma Barrage. Water quality parameters indicated suitability for fish growth and health. Heavy metal concentrations were determined using an atomic absorption spectrometer. Results indicated elevated levels of Cd, Cr, and Cu in sediment and Pb and Cd in water, surpassing WHO standard limits. Among the fish species, bottom feeder ( C. mrigala ) exhibited significantly ( P  < 0.05) higher heavy metal levels in its tissues (gills, liver, and muscle) compared to column feeder ( L. rohita ) and surface feeder ( C. catla ). Liver tissues across all species showed higher heavy metal bioaccumulation, followed by gills. Principal component analysis (PCA) revealed strong correlations among heavy metals in sediment, gills, muscle, and water in every fish species. However, the vector direction suggests that Cr was not correlated with other heavy metals in the system, indicating a different source. The human health risk analysis revealed lower EDI, THQ, and HI values (< 1) for the fish species, indicating no adverse health effects for the exposed population. The study emphasizes the bioaccumulation differences among fish species, underscoring the higher heavy metal concentrations in bottom feeder fish within Chashma Barrage.

Introduction Total knee arthroplasty is the standard of care treatment for advanced knee osteoarthritis. However, patients frequently continue to have pain and disability after surgery, with one of the most common reasons being a bone-implant mismatch. Notably, there is a significant difference reported in proximal tibia morphometry between Asian and Caucasian populations, and the currently available implants do not account for the anthropometric variations observed across ethnicities. We aimed to evaluate the proximal tibia anthropometry in a Pakistani population. Materials and methods A study was conducted at The Indus Hospital, Karachi Campus, from August 2019 to July 2020. All consecutive patients fulfilling the eligibility criteria and undergoing knee replacement surgery were included in the study. Baseline characteristics and anthropometry of proximal tibia were recorded on a pre-designed proforma. Statistical analysis was done using SPSS version 24. Results  A total of 30 patients were enrolled in this study, which included 17 females (56.7%) and 13 males (43.3%). The mean age was 61.6± 7.9 years and the BMI was 33±5.7 kg/m2. There was a significant difference found in the anteroposterior and mediolateral dimensions in both genders. A significant association was noted with body mass index (p-value 0.01) and occupation (p-value=0.02). Conclusion The results indicated that the anatomical profile of the proximal tibia in the Pakistani population is distinct, thus stressing the fact that it requires developing prostheses specifically tailored to this population's sizing requirements.