In IoT networks, traffic data are high-dimensional, complex, and highly imbalanced. Minority attack classes are often underrepresented degrading the detection performance. In addition, manual labeling of large-scale IoT traffic is costly, and network behavior continuously changes over time. To overcome these challenges, this paper presents an adaptive, secure and explainable IoT intrusion detection framework. First, a generative AI based data balancing method is applied using the real-valued non-volume preserving model. It generates realistic minority-class samples while preserving the original data distribution. Then, a hybrid Deep Dual-Attention Network (DDANet) is proposed. It combines a Deep Neural Network (DNN) with a Dual Attention Network (DANet). The DNN captures global nonlinear features. The DANet focuses on important channel and spatial features. This improves feature learning and intrusion detection accuracy. Next, Active DDANet (ADDANet) integrates a monte-carlo based active learning strategy. It selects informative samples using predictive uncertainty to reduce redundant labeling and improves learning efficiency. Reinforced DDANet (RDDANet) further integrates an actor-to-critic reinforcement learning mechanism. It enables adaptive optimization in dynamic IoT environments and stabilizes training. A blockchain layer is also integrated. It ensures secure logging, decentralized trust, and tamper-resistant storage of detection results. This increases transparency and reliability in distributed IoT systems. Experimental results show that DDANet improves accuracy, recall, and F1-score by 3.33%, precision by 3.30%, Cohen's Kappa (CK) and Mathew's Correlation coefficient (MCC) by 4.60%, and Precision Recall Area Uner the Curve (PR-AUC) by 3.16% over baseline models. It also reduces Log Loss (LL) by 34.38% and Hamming Loss (HL) by 30.00%. ADDANet further improves accuracy, recall, and F1-score by 3.23%, CK and MCC by 3.30%, and reduces LL by 38.10% and HL by 42.86% over DDANet. RDDANet achieves similar accuracy improvement of 3.23%, increases CK and MCC by 4.40%, and provides the highest LL reduction of 42.86% and HL reduction of 42.86%. The framework is validated using 10-fold cross-validation to ensure robustness and generalization. Confidence interval analysis and paired t-test confirm statistical significance. Finally, integrated gradients based explainable AI is applied to provide feature attribution scores and improve model transparency. The proposed framework provides an adaptive, secure, explainable, and high-performing IoT intrusion detection solution.

Blockchain technology offers significant advantages in securing the internet of things (IoT) networks. However, IoT devices remain highly vulnerable to security and privacy threats, making them prime targets for malicious activities. This study addresses key challenges in IoT security, including ensuring device authenticity, preserving data integrity through decentralized storage, and enhancing the explainability of predictive models. To tackle these challenges, a novel approach integrating blockchain and machine learning (ML) is proposed. A stacking-based classification model is introduced to differentiate between legitimate and malicious IoT entities. At the base layer, the model leverages the extra trees, multinomial Naive Bayes, and Bernoulli Naive Bayes classifiers, while the logistic regression with cross-validation classifier functions as the meta-model. The preprocessing pipeline includes data normalization and handling of missing values to improve model robustness. To further strengthen security, a local blockchain is implemented on an IoT device manager to register IoT requestors with unique addresses. The Keccak256 hashing algorithm converts these addresses into hashes, which are securely stored on the local blockchain. The actual data is managed using the interplanetary file system, while block validation is performed using a proof-of-stake consensus mechanism. The proposed model classifies IoT devices with superior performance compared to baseline classifiers. Experimental results demonstrate the effectiveness of the stacking model, achieving notable improvements: a 6.90% increase in macro-recall, a 4.49% improvement in the Matthews correlation coefficient and Cohen’s kappa, a 3.33% enhancement in the macro-F1-score, and approximately a 1.02% gain in accuracy, micro-precision, micro-recall, and area under the receiver operating characteristics curve. Additionally, log loss and Hamming loss are reduced by 50%, indicating enhanced reliability and lower error rates. Results of the proposed stacking model are further assessed using the Friedman statistical test and 10-fold cross-validation techniques. To ensure interpretability, Shapley additive explanations and local interpretable model-agnostic explanations are employed, providing insights into model decisions. These findings underscore the effectiveness of the proposed approach in improving IoT security by combining blockchain for decentralized authentication and explainable ML for transparent decision-making.

The Internet of Things (IoT) has revolutionized intelligent networked ecosystems; however, the massive interconnectivity of heterogeneous devices has simultaneously amplified the attack surface, leading to complex cybersecurity challenges. This study presents an advanced IoT intrusion detection framework integrating data preprocessing, synthetic oversampling, and hybrid deep learning optimization. Initially, categorical attributes and target variables are numerically transformed using label encoding to ensure computational compatibility. Subsequently, the Proximity Weighted Synthetic Oversampling (ProWSyn) technique is employed to generate boundary-sensitive synthetic samples, effectively addressing class imbalance. Min-Max scaling is then applied to normalize feature magnitudes, improving convergence stability across models. The proposed Dual Attention Pointer Network (DAP-Net) combines the attention-guided feature refinement of the Dual Attention Network (DANet) with the sequence-to-point mapping efficiency of Pointer Network (PointerNet) in a parallel hybrid configuration. Furthermore, the Dual Attention Pointer Whale Optimized Network (DAP-WONet) enhances DAPNet through hyperparameter tuning using the Whale Optimization Algorithm (WOA), thereby strengthening its generalization and robustness. Experimental results on the CICIoT2023 dataset demonstrate that DAPNet achieves an accuracy of 0.8247, surpassing DANet by 4.68% over PointerNet. The optimized DAPWONet further elevates performance, attaining an accuracy of 0.8662, reflecting improvements of 9.93% and 8.46% over DANet and PointerNet, respectively. In terms of precision, recall, F1-score, and PR-AUC, DAPWONet achieves respective gains of 11.82%, 8.93%, 9.99%, and 7.94% compared to DANet. These outcomes affirm that the proposed models effectively capture complex feature dependencies, minimize prediction uncertainty, and deliver superior classification stability across diverse IoT intrusion categories.

Serine proteases are important enzymes widely used in commercial products and industry. Recently, we identified a new serine protease from the desert bacterium Bacillus subtilis ZMS-2 that showed enhanced activity in the presence of Zn2+, Ag+, or H2O2. However, the molecular basis underlying this interesting property is unknown. Here, we report comparative studies between the ZMS-2 protease and its homolog, subtilisin E (SubE), from B. subtilis ATCC 6051. In the absence of Zn2+, Ag+, or H2O2, both enzymes showed the same level of proteolytic activity, but in the presence of Zn2+, Ag+, or H2O2, ZMS-2 displayed increased activity by 22%, 8%, and 14%, whereas SubE showed decreased activity by 16%, 12%, and 9%, respectively. In silico studies showed that both proteins have almost identical amino acid sequences and folding structures, except for two amino acids located in the protruding loops of the proteins. ZMS-2 contains Ser236 and Ser268, whereas SubE contains Thr236 and Thr268. Replacing Ser236 or Ser268 in ZMS-2 with threonine resulted in variants whose activities were not enhanced by Zn2+ or Ag+. However, this single mutation did not affect the enhancement by H2O2. This finding may be used as a basis for engineering better proteases for industrial uses.

Nowadays, the Internet of Things (IoT) networks provide benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data to the destination. However, the network nodes’ connectivity is affected by the nodes’ removal caused due to the malicious attacks. The ideal plan is to construct a topology that maintains nodes’ connectivity after the attacks and subsequently increases the network robustness. Therefore, for constructing a robust scale-free network, two different mechanisms are adopted in this paper. First, a Multi-Population Genetic Algorithm (MPGA) is used to deal with premature convergence in GA. Then, an entropy based mechanism is used, which replaces the worst solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of Edge Swap Mechanisms (ESMs) are proposed. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency. While the second ESM named as EESM-Assortativity, transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree network nodes. Further, Hill Climbing (HC) and Simulated Annealing (SA) methods are used for optimizing the network robustness. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, both the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they increase the graph density as well as network’s connectivity.

Introduction: Coronary Artery Diseases (CAD) have been estimated to be the leading cause of mortalities in developing countries in the year 2010. It is evident from previous studies that healthy life style choices can help in the reduction of CAD risk factors. They are either modifiable risk factors including hypertension, hypercholesterolemia, smoking, diabetes mellitus, obesity, and psychological stress or non-modifiable risk factors such as age, gender, family history and ethnicity. Non modifiable risk factors, however, are not studied in detail regarding their association with CAD. Objective: The objective of this study was to assess non-modifiable risk factors associated with CAD in patients and to devise strategies to deal with these risk factors to reduce the incidence of CAD among local population. Methodology: Retrospective observational study was conducted at Peshawar Institute of Cardiology (PIC). Data was extracted from Electronic Medical Records (EMR) of 98 patients including non-modifiable risk factors associated with CAD i.e., gender, age and family history. Results: The analysis performed indicated that in patients with CAD, non-modifiable risk factors such as age and gender play a significant role (p

Microbial alkaline proteases are dominating the global enzyme market with a share of over 65

Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

Background: Liver parenchyma infection by bacteria, fungi and parasitic organisms are common in HPB practices. Microbiology spectrum and management have changed over the past decade. Currently published data sparse in Africa and other LMICs Purpose: Descriptive analysis of experience with infected liver collections from 2 HPB referral centres in South Africa Method: Analysis of databases of Chris Hani-Baragwanath Academic (CHBAH) and Charlotte Maxeke Johannesburg Academic (CMJAH) hospitals and the South African National Health Laboratory Services (NHLS) records over a 5-year period. Clinicopathologic and radiology features and management were analysed. Continuous variables were as mean and ranges with categorical variables as percentages. Mann-Whitney and one way ANOVA tests for differences between groups. p values of <0.05 considered statistically significant. Results: Data of 213 patients were analyzed. Males predominated (54%) with mean age 48.8 years. HIV (24%) and diabetes (16,1%) were main comorbidities identified. 68% had pyogenic abscesses, whilst amoebic and hydatid cysts occurred in 16% and 9.2% respectively. 41% of pyogenic abscesses were cholangitic (biliary causes). 8,2% of blood cultures and 32,2 % abscess cultures were positive in the pyogenic cases. Klebsiella, E. coli and Streptococci were main isolates whilst 8% were poly microbial. Percutaneous drainage was sufficient for 68% abscesses, whilst 11% (mainly hydatid) underwent surgery. Average hospitalisation was 16 days, with a mortality rate of 3,2%. Conclusions: In this study, pyogenic abscesses, mainly of biliary etiology predominated in middle aged males. HIV co-infection was present in a quarter of patients. Non-surgical management was sufficient in majority of cases.

Proteolytic enzymes are the most versatile and commercially viable group of enzymes comprising over 65% share in the global enzyme market amongst which alkaline proteases have extensive applications in detergent and leather industry. Current study was designed to assess the potential of an alkaline serine protease from Bacillus subtilis ZMS-2 as a bating agent in leather processing. Initially, the production parameters were investigated through Response Surface Methodology (RSM) using Plackett-Burman Design, which identified substrate, agitation speed and incubation temperature as the most significant factors. The optimal levels of these factors were determined through the Box-Behnken experimental analysis as 0.436% substrate concentration, 36.5 °C incubation temperature and 56 rpm agitation speed. The statistical optimization experiments increased the volumetric production of enzyme by 3.94 times (2246 U mL−1) than the initial titer (571 U mL−1). The enzyme was partially purified and characterized as metal ions and detergent compatible serine protease having optimum activity at pH 8 and 60 °C. During the pilot-scale application as a bating agent, the enzyme (340 U mL−1) successfully removed the hair roots and other unwanted proteins from goat skins as observed during scudding and confirmed through Scanning Electron Microscopy. The processed skins displayed enhanced porosity, thumb impression, smoothness and pliability. These findings provide a strong basis for the use of this protease as an efficient and eco-friendly alternative for bating of animal skins in leather tanneries.

A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the base of automation. However, the increased number of sensors make the IoT networks more complex and scaled. Due to the increasing size and complexity, IoT networks of scale-free nature are found highly prone to attacks. In order to maintain the functionality of crucial applications, it is mandatory to increase the robustness of IoT networks. Additionally, it has been found that scale-free networks are resistant to random attacks. However, they are highly vulnerable to intentional, malicious, deliberate, targeted and cyber attacks where nodes are destroyed based on preference. Moreover, sensors of IoT network have limited communication, processing and energy resources. Hence, they cannot bear the load of computationally extensive robustness algorithms. A communication model is proposed in this paper to save the sensors from computational overhead of robustness algorithms by migrating the computational load to back-end high power processing clusters. Elephant Herding Robustness Evolution (EHRE) algorithm is proposed based on an enhanced communication model. In the proposed work, 6 phases of operations are used: initialization, sorting, clan updating, clan separating,selection and formation, and filtration. These process collectively increase the robustness of the scale-free IoT networks. EHRE is compared with well-known previous algorithms and is proven to be robust with a remarkable lead in performance. Moreover, EHRE is capable to achieve global optimum results in less number of iterations. EHRE achieves 95% efficiency after 60 iterations and 99% efficiency after 70 iterations. Moreover, EHRE performs 58.77% better than Enhanced Differential Evolution (EDE) algorithm, 65.22% better than Genetic Algorithm (GA), 86.35% better than Simulating Annealing (SA) and 94.77% better than Hill climbing Algorithm (HA).

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

Most smartphones and tablets have either been produced or are about to be released, and the Android operating system is swiftly gaining market share. These days, customers utilize Android applications often for a broad variety of tasks. As a result, attackers now frequently target the Android platform. Many harmful applications have been discovered in Information technology, and they frequently act maliciously in ways that don't correspond to their intended characteristics. Therefore, it's essential to identify harmful Android applications. This article describes multiple techniques for identifying fraudulent programmers using app permissions.

Skin cancer is one of the most common and dangerous diseases due to a lack of awareness of its signs and methods for prevention. Skin cancer disease can be counted as a fourth burden disease around the world, with the rate of deaths dramatically growing globally. Therefore, early detection at an early stage is necessary to stop the spread of cancer. In this paper, we detect and classify multi-label skin cancer and implement the optimal techniques using machine learning and image processing approaches. However, preprocessing methods assist in removing irrelevant and unnecessary features from the label encoder, and standard features are applied to standardize the range of functionality by scaling the input variance unit. Moreover, various machine learning techniques were applied to check the performance of every classifier on the HAM10000_metadata dataset. The experimental analysis was conducted on the HAM10000_metadata dataset, which consists of seven different types of skin cancer. The results analysis shows that machine learning algorithms such as SVM, DT, and GNB obtained the highest accuracy compared to the other classifiers.

HIV is associated with various diseases of the liver and biliary tree. The aim of this study was to review our experience with ERCP in patients presenting with biliary disease and co-infected with HIV. A secondary aim was to look for pre-ERCP predictive factors of aids related cholangiopathy (ARC). All patients who underwent an ERCP at an academic hospital in Johannesburg, South Africa between 2013 and 2021 were included. Descriptive statistics were used. Patients variables were compared between patients with or without ARC using chi-square and indepent samples t-test as appropriate. A total of 285 ERCPs were performed on 236 patients. 65% were female. The median age was 45 (SD 10.9). 38 patients underwent more than 1 ERCP (Range 2-4). 30% of patients had choledocholithiasis, 22% had ARC, 27% had biliary tract or pancreatic malignancies. Age was the only pre-ERCP variable that predicted the presence of ARC (p=0.015). Patients with ARC were significantly younger than patients with other pathology. CD4 count at time of ERCP did not predict the presence of ARC (p=0.08). This large study of patients with HIV undergoing ERCP found that CD4 count does not predict the presence of ARC. Further linear studies are likely to be required to evaluate changes in CD4 count and the development of HIV related biliary disease.

The research work was carried out for sub-seismic transverse faults & fractures corridor identification, its characteristics and impact on wells placement & address failure reasons of 02 dry wells. On 3D-Seismic data, a structural model was developed and converted to a Geomechanical model after incorporating the carbonates rock geo-mechanical properties. Through forward modeling, the valid Geomechanical model was exercised by Elastic Dislocation (ED) theory (Solutions of Okada, 1985, 1992). From mapping fault geometry and slip distribution in 2D/3D seismic-reflection datasets, the application of ED theory can forecast the distribution of displacement, strain, and stress in the rock volume near major faults. The total strain, including background or remote strain as well as strain from displacement along the fault surface, was calculated. For deformation horizons, this total strain determines stress and failure. The fracture sets were generated from failure planes in ED modeling. The majority are reverse fracture sets. The fault surfaces play a major role in the ED method for fracture analysis and consists of an array of panels, each contributing to the ED equation calculation. The main outcome is the sub-seismic faults & fractures corridor identification around larger faults. The ED model displays that the majority of predicted sub-seismic faults & fractures set orientation match to the major strike of regional fault planes, while some vary along the strike, being parallel to the main reverse fault directions (E-W or NW-SE), but oblique faults system is also existed along the central segments, agreeing with observed structure dip variations along the strike of faults. The thrusted anticlines' crestal region has more fracture densities than their flanks. Observing abrupt change of sub-seismic faults & fractures sets density & orientation in the Geomechanical model result, it was concluded that some transverse faults systems may exist sub-seismically, which is almost parallel to the compressional forces (N-S) direction and opposite to seismic scale faults trends (E-W or NW-SE) and the majority of predicated sub-seismic faults & fractures sets orientation. Secondly, in ED modeling we have noticed that well-01 (Producing) falls in high fractures density and well-02 & 03 (Dry) falls in low fractures density also having variation in fracture strike orientation, which is verified by the well's FMI results also. The well planning to drill out the max fractures sets with proper face in the carbonate reservoir is a challenging job. The Eocene carbonate reservoirs in the studied fields have low primary porosity and permeability. The productivity of these reservoirs is dependent on permeable natural sub-seismic fractures. In the case study, an integrated approach was applied in the form of Geomechanical modeling based on ED theory to provide a reliable base for future well planning and have found out the failure reason of wells.

Liver resection for colorectal liver metastases (CRLM) is established. Early onset colorectal cancer (EOCC), age <50 years is increasing. There are few reported outcomes of liver resection in this population. Methods: The prospectively maintained database of liver resections for CRLM in Leeds was analysed from 2010-2019. Descriptive statistics, log rank and cox proportional hazard models were used as appropriate. Results: A total of 84 patients under 50 years of age underwent 104 operations to resect CRLM. The mean age was 42 years. 69% of patients presented with synchronous CRLM. The sequence of surgery was resection of primary followed by liver resection in 75 patients, combined resection in 6, and liver first approach in 3 patients. Nineteen patients had 1 or more additional operations for recurrent CRLM. There were thirteen Clavien Dindo grade 3 complications and no peri-operative mortality. Median overall survival was 70 months. The following variables were subjected to univariate analysis: gender, primary tumour site, nodal status, tumour grade, mutational analysis, major resection, timing of metastases, number of resected CRLM, size of resected CRLM, liver resection margin and repeated resections for metastases. Margin status (p = < 0.001), number of resected metastases >4 (p = 0.024) and metastatic tumour vascular invasion (p = 0.033) were associated with a significantly poorer overall survival. Multivariate analysis found margin status (p=0.006), metastatic vascular invasion (p=0.005) remained significant. Conclusion: This study demonstrates that patients with EOCC have at least equivalent outcomes following resection of CRLM when compared to average onset colon cancer

The study will address the failure reasons of wells and point out the high-density fracture zones, to drain out the remaining hydrocarbons in the field. A robust 3D geological model was developed based on 3D seismic interpretation. The rock mechanical properties of carbonates were incorporated. The total strain i.e., the background or remote strain (Bulk deformation) and the strain from displacement along the fault surfaces are mapped to each segment/element of the generated fault surfaces. This total strain calculates stresses and the failure for deformation surface. The geomechanical model based on Elastic Dislocation (ED) theory identified strain fields on horizon surface / observation grids and then finally fractures corridor and their characteristics i.e. distribution, orientation. Fault planes generated from interpretation play a major role in the ED method for fracture analysis. The fault surface consists of an array of panels, each contributing to the ED equation calculation. The main outcome is the sub-seismic faults and fractures identification around larger faults on the horizontal observation medium. The identified fractures corridors characteristics, distribution and orientation changes along the strike of the major fault system. In the developed ED model the predicated fractures system are parallel to the major reverse fault direction, but oblique fractures corridor is also observed along the middle segments, aligning with observed variations in structure dip. The crestal portion of the anticline has a higher density of fractures than the rim. The ED modelling fractures results were verified against FMI data of the targeted horizon, which demonstrated that the wells which were drilled in high-density fracture zones (modelled) have produced hydrocarbons and vice versa. There is a correlation between modelled results with image logs and well-testing results (DST's), which increases the reliance on the ED theory's ability to correctly identify small-scale (sub-seismic) fractures, joints and faults system. The Eocene carbonate reservoirs have low primary porosity and permeability. The productivity of these reservoirs is dependent on permeable natural fractures and sub-seismic faults. The identification of these features is a major problem before drilling while, conventional techniques do not provide optimum solutions to their understanding. A case study of compressional tectonic regime in Himalayan fold & thrust belts is presented here, where an integrated approach is applied in the form of geomechanical modelling, which is built on the ED theory provide a reliable base for well planning.

Precipitation elasticity provides a basic estimate of the sensitivity of long-term streamflow to changes in long-term precipitation, and it is especially useful as the first assessment of climate change impact in land and water resource projects. This study estimated and compared the precipitation elasticity (εp) of streamflow in 86 catchments within Pakistan over 50 major rivers using three widely used analytical models: bivariate nonparametric (NP) estimator, multivariate NP analysis, and multivariate double logarithm (DL) model. All the three models gave similar values of elasticity in the range of 0.1–3.5 for over 70–75% of the catchments. This signifies that a 1% change in the annual mean precipitation compared to the long-term historic mean annual precipitation will amplify the streamflow by 0.1–3.5%. In addition, the results suggested that elasticity estimation of streamflow sensitivity using the multivariate DL model is more reliable and realistic. Precipitation elasticity of streamflow is observed high at altitudes ranging between 250 m and 1000 m while the longitudinal and latitudinal pattern of εp shows higher values in the range of 70–75 and 32–36 decimal degrees, respectively. The εp values were found to have a direct relationship with the mean annual precipitation and an inverse relationship with the catchment areas. Likewise, high εp values were noticed in areas where the mean annual temperature ranges between 15 and 24 °C.

Empirical evidence to identify factors that are responsible for the sluggish development of bond and capital markets in Pakistan remains scanty. This paper is a step forward in this direction. Specifically, this paper draws on the recent developments in the area of law and finance to formulate several propositions on how judicial efficiency can have a differential impact on corporate capital structures of small and large firms. These propositions are tested using data of 370 firms listed at the Karachi Stock Exchange (KSE) and 27 districts high courts of Pakistan. The results indicate that leverage ratio decreases, when judicial efficiency decreases; however, this relationship is not statistically significant. This is due to the composition effect. Allowing judicial efficiency to interact with the included explanatory variables, the results show that worsening judicial efficiency increases leverage ratios of large firms and decreases leverage ratios of small firms, which is an indication of the fact that creditors shift credit away from small firms to large firms in the presence of inefficient judicial system. Results also indicate that the effect of inefficient courts is greater on leverage ratios of firms that have fewer tangible assets as percentage of total assets than on leverage ratios of firms that have more tangible assets. The results indicate that under inefficient judicial system creditors reduce their lending to small firms and firms with little collateral and redistribute the credit to large firms. This is why judicial inefficiency does not change volume of credit, but changes distribution of the credit. These results highlight the importance of judicial efficiency for small firms in the determination of their capital structures. JEL Classification: G10, G21, G32 Keywords: Judicial Efficiency, Leverage, KSE, Capital Market Development, Law and Finance.