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Abstract Background Coccinella septempunctata (L.) is an important predator in fighting against aphids on edible crops and vegetables. However, to establish a successful mass-rearing technique, it is crucial to determine the optimal diets for larvae, pupae, and adults in terms of fecundity, longevity, and ingredient ratios. The current study aims to learn more about the biology of ladybird beetle using both artificial and natural food components. Results This study tested six different ratios of natural and artificial diets, ranging from a natural aphid diet to a variety of artificial diets containing protein hydrolyzate, casein protein, honey, dry aphids, yeast, and agar. The results showed that the natural diet (D1) produced from aphids showed approximately 290.2 eggs, while artificial diets D2 and D4 produced 56.5 and 40.2 eggs, respectively. The effect of different diets on the longevity of different life stages was also tested. The minimum longevity of egg, larval, pupal, and adult stages were recorded for D1 and D4. Furthermore, the maximum percentage emergence of larval, pupal, and adult stages was observed for D1 and D2. D6 produced the longest adult duration, with a maximum of 84.60 days. However, there was no significant difference in sex ratio among the various diet components. Conclusions In conclusion, the results suggest that artificial diets are essential for sustaining C. septempunctata cultures in laboratories, especially during periods when natural prey is scarce. The most effective and efficient mass-rearing approach would be to provide both natural and artificial foods simultaneously. These findings may have significant implications for the development of biological control strategies for aphids in agriculture.

Abstract As cloud services expand, the need to improve the performance of data center infrastructure becomes more important. High-performance computing, advanced networking solutions, and resource optimization strategies can help data centers maintain the speed and efficiency necessary to provide high-quality cloud services. Running containerized applications is one such optimization strategy, offering benefits such as improved portability, enhanced security, better resource utilization, faster deployment and scaling, and improved integration and interoperability. These benefits can help organizations improve their application deployment and management, enabling them to respond more quickly and effectively to dynamic business needs. Kubernetes is a container orchestration system designed to automate the deployment, scaling, and management of containerized applications. One of its key features is the ability to schedule the deployment and execution of containers across a cluster of nodes using a scheduling algorithm. This algorithm determines the best placement of containers on the available nodes in the cluster. In this paper, we provide a comprehensive review of various scheduling algorithms in the context of Kubernetes. We characterize and group them into four sub-categories: generic scheduling, multi-objective optimization-based scheduling, AI-focused scheduling, and autoscaling enabled scheduling, and identify gaps and issues that require further research.

Objective: To compare porcelain and metal repair done with both nanocomposite and conventional composite. Material and Methods: A total of 30 cylinders were fabricated from Porcelain (I), Porcelain fused to metal (II), and metal (III) substrate each. Control group (A) was bonded with conventional micro-hybrid composite and experimental group (B) was bonded with nanocomposite in a 2 mm thickness. All specimens were thermocycled and stored in distilled water at 37 °C for 7 days. A universal testing machine was used to measure the Shear bond strength (SBS). The difference between bond strengths of the groups was compared using an independent t-test. Results: In all three groups, the SBS was higher in the experimental group as compared to the control group. The use of nanocomposite of metal alloy presented maximum shear bond strength, followed by samples of porcelain fused to metal and finally porcelain, showing the lowest values of SBS. Conclusion: Porcelain and alloys bonded with nanocomposite exhibit enhanced adhesiveness as well as aesthetic and mechanical properties. This subsequently would translate into providing higher clinical serviceability and durability and hence a cost-effective and accessible repair option for human welfare.

Identifying the temperature change at a regional level is one of the essential parameters to determine theintensity of climate change. The current investigation provides an examination of changing trends of temperature in thePunjab province from 1970 to 2019. Sen's slope estimator method is applied to monthly data of mean temperature(Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) to calculate the rate of temperature change.Statistical methods were used to find out the level of significance in terms of negative or positive trends to examine thevariability among various weather observatories. Moreover, predicted values have also been observed for a detailedanalysis of temperature variability and trends. Significant and pronounced changes in the mean temperature (T mean) are distinguished all over the Punjab regions with an increasing trend from North to South Punjab. In the case of maximum temperature (Tmax), a faster rate of rising in temperature is observed over the Southern and Western regionsof Punjab. In contrast, the minimum temperature (Tmin) shows an increasing trend in Central Punjab. The findings provide detailed insight to policymakers for the planning of mitigating efforts and adaptation strategies in response to climate change.

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.

Abstract Two-dimensional (2D) heterostructure with atomically sharp interface holds promise for future electronics and optoelectronics because of their multi-functionalities. Here we demonstrate gate-tunable rectifying behavior and self-powered photovoltaic characteristics of novel p-GeSe/n-MoSe2 van der waal heterojunction (vdW HJ). A substantial increase in rectification behavior was observed when the devices were subjected to gate bias. The highest rectification of ~ 1 × 104 was obtained at Vg = − 40 V. Remarkable rectification behavior of the p-n diode is solely attributed to the sharp interface between metal and GeSe/MoSe2. The device exhibits a high photoresponse towards NIR (850 nm). A high photoresponsivity of 465 mAW−1, an excellent EQE of 670%, a fast rise time of 180 ms, and a decay time of 360 ms were obtained. Furthermore, the diode exhibits detectivity (D) of 7.3 × 109 Jones, the normalized photocurrent to the dark current ratio (NPDR) of 1.9 × 1010 W−1, and the noise equivalent power (NEP) of 1.22 × 10–13 WHz−1/2. The strong light-matter interaction stipulates that the GeSe/MoSe2 diode may open new realms in multi-functional electronics and optoelectronics applications.

Background and Objective: One of the major requisites of an ideal restorative material is excellent marginal seal. Microleakage has been identified as a significant clinical problem with filling materials. The objective of the study was to evaluate and compare the degree of microleakage of amalgam, high viscosity glass ionomer and resin-modified glass ionomer in primary molars.Methods: A total of 99 human non-carious primary molars that were liable to exfoliate within the next six months were selected. Standardized Class I cavities were prepared on occlusal surfaces of molars and randomly filled with either Amalgam (Aristaloy 21), High Viscosity Glass Ionomer (Ketac Molar) or ResinModified Glass Ionomer (GC Fuji II LC). The teeth were divided into nine groups (n=11 each) on the basis of filling material used and time of restored tooth extraction viz. one week, six weeks and twelve weeks. After immersion in a 2% methylene blue dye solution for 24 hours, the teeth were sectioned buccolingually in an occluso-apical direction through the middle of the restoration. The specimens were then examined under stereomicroscope at 30X magnification. Statistical analyses were performed with ANOVA and Tukey’s test at 5% level of significance.Results: Amalgam restorations had significantly less microleakage as compared to the high-viscosity glass ionomer and resin-modified glass ionomer restorations. High-viscosity glass ionomer restorations were superior in resisting microleakage than resin-modified glass ionomers. The restorative materials used can be arranged in their sealing ability from least to severe microleakage as: Amalgam Conclusion: Amalgam restorations showed less microleakage as compared to high-viscosity and resinmodified glass ionomer restorations. Amalgam may preferably be used as a restorative material of choice for primary molars.

The tremendous advancement in embedded systems, miniaturization, and wireless technology had allowed Wireless Body Area Networks (WBAN) to have overwhelming applications in e-healthcare, entertainment, sports/games training, etc. WBAN is a special type of wireless sensor network where bio-sensors are attached or embedded to a single human-body designed to connect various bio-sensors and applications, operate autonomously and observe different vital signs of a human body remotely. Despite its enormous benefits and applications, some of the key challenges in designing heterogeneous WBAN is their energy-efficiency, reliability, and fault-tolerance among the installed bio-sensors. Due to the criticality of services related to WBAN applications, it is imperative to have a high degree of reliability and fault-tolerance, especially in the case of health-care monitoring applications where continuous monitoring of patient's vital information is required for diagnosis. However, in health-care applications, interference and body fading occur, which affect the communication among nodes and gateway, which reduces the reliability and fault-tolerance of the network. To address these issues, in this paper, we have proposed an energy-efficient fault-tolerant scheme to improve the reliability of WBANs. The proposed scheme adopted the cooperative communication and network coding strategy to minimize channel impairment and body fading effect and hence reduces the ensued faults, bit error rate, and energy consumption. Based on the proposed scheme, a case study was designed for remote Sepsis monitoring. The system identifies tracking indicators using cooperative communication to reduce hospital re-admissions and mortality rates. The proposed scheme performance is also evaluated via extensive simulations using various metrics. From the results obtained, it is evident that the proposed scheme reduces energy consumption, delay, and bit error rate, thereby increasing the throughput and reliability in WBAN.

Wireless Sensor Network (WSN) is one of the fundamental technologies used in the Internet of Things (IoT) which is deployed for diverse applications to carry out precise real-time observations. The limited resources of WSN with massive volume of fast-flowing IoT data make the aggregation and analytics of data more challenging. Recently, data mining-based solutions have been proposed to effectively handle the data being generated by the sensors and to analyze the data patterns for deducing the required information from it. The increasing need of these techniques motivated us to propose a distributed and efficient data mining technique that not only handles the massive and rapidly generated data by the nodes, but also increases the life span of the network. In this paper, we propose a novel scheme for the IoT based WSN that mines the sensor data using association rule without moving it to any Cluster Head (CH) or Base Station (BS). The new proposed scheme enables sensors to perform computations locally and only the minimum higher-level statistical summaries of the data at Cluster Members (CMs) are exchanged with their CH. This considerably reduces the communication overhead which ultimately prolongs the network lifetime. The proposed scheme is evaluated via extensive simulations and the results obtained demonstrate that the integration of the proposed scheme in the existing protocols significantly reduces the communication overhead which ultimately prolongs the network lifetime and stability.

The security of a community is its topmost priority; hence, governments must take proper actions to reduce the crime rate. Consequently, the application of artificial intelligence (AI) in crime prediction is a significant and well-researched area. This study investigates AI strategies in crime prediction. We conduct a systematic literature review (SLR). Our review evaluates the models from numerous points of view, including the crime analysis type, crimes studied, prediction technique, performance metrics and evaluations, strengths and weaknesses of the proposed method, and limitations and future directions. We review 120 research papers published between 2008 and 2021 that cover AI approaches for crime prediction. We provide 34 crime categories researched by researchers and 23 distinct crime analysis methodologies after analyzing the selected research articles. On the other hand, we identify 64 different machine learning (ML) techniques for crime prediction. In addition, we observe that the most applied approach in crime prediction is the supervised learning approach. Furthermore, we discuss the evaluation and performance metrics, as well as the tools utilized in building the models and their strengths and weaknesses. Crime prediction AI techniques are a promising field of study, and there are several ML models that researchers have applied. Consequently, based upon this review, we provide advice and guidance for researchers working in this area of study.

Climate science has confirmed the alteration of the hydrological cycle attributed to global warming. This warming tendency affects the monsoon precipitation in Pakistan with an unprecedented intensity, causing severe flooding. Therefore, it is inevitable to observe the recent spring and summer monsoon changes in extreme precipitation throughout Pakistan. The present study examined 8 precipitation indices in the past 50-year period (1971–2020) (stretched to two data periods; 1971–1998 and 1999–2020) using Mann–Kendall and Sen’s method to investigate the direction and magnitude of the observed trends. Spring and summer wet days significantly increased in the central eastern (Kakul, Kotli, Jhelum) and western (Cherat, Chitral, Peshawar) regions in the 1st data period but significantly decreased in areas including the southern region in the 2nd data period. We further observed the high-intensity precipitation days (R10, R20) in the same seasons. The intensity of summer R20 was much stronger throughout Pakistan in the 1st data period which reduced significantly during the 2nd data period in northern and southern regions. We extended the circle of investigation to very heavy and extreme precipitation (R30 and R50). The intensity of R30 and R50 in summer followed the same pattern as observed for R10 and R20. However, R30 and R50 in pre-monsoon significantly increased in the northern, east-western, and south-eastern regions during the 2nd data period. Summer monsoon and westerly humid regions experienced a decreasing tendency of very heavy and severe precipitation in the 1st data period. Our results concluded that the most significant changes in precipitation extremes occurred with higher intensity and recurring frequency for all indices in spring and summer monsoon during the 2nd data period.

The Hindukush-Karakoram-Himalayan (HKH) ranges and their massive cryosphere extend over the Upper Indus Basin (UIB) and are prone to incapacitated water supply due to the proclivity of globally increased temperature. Due to excessive carbon emissions, frequent incursions including extreme climatic events, are likely to happen sooner than expected on a regional scale due to recent climate change. The present study examined the variability of climatic extremes (18 indices) during 1971 to2018 over the UIB. The Mann-Kendall (MK) test and Sen’s methods were applied for statistical analysis as the former deals with the magnitude of trends while the direction of observed trends was identified by the latter in climatological time-series data. The frequency and intensity of summer days (SU25 > 25 °C/year) at 13 out of 27 stations significantly increased, particularly in lower regions. The same warming proclivity was dominant in tropical nights (TR20 > 20 °C/year) at 20 stations including Astore, Bunji, Gilgit, Gupis, Murree and Skardu. Similarly, significant increases were observed in extremes of annual precipitation in western and high northern areas; however, significantly, the highest drops in R25 and R5day were exhibited in Chitral at the rates of 13 and 29 days, respectively. These findings tend to support the accelerated summer warming and a rather stable winter warming while stable winter warming showed that overall the UIB seems to be more sensitive towards warming.

The Upper Indus Basin (UIB) features the high mountain ranges of the Hindukush, Karakoram and Himalaya (HKH). The snow and glacier meltwater contribution feeds 10 major river basins downstream including Astore, Gilgit, Hunza, Jhelum, Kabul, Shyok and Shigar. Climate change is likely to fluctuate the runoff generated from such river basins concerning high and low streamflows. Widening the lens of focus, the present study examines the magnitude and timing of high flows variability as well as trends variability in low streamflows using Sen’s slope and the Mann-Kendall test in UIB from 1981 to 2016. The results revealed that the trend in the magnitude of the high flows decreased at most of the sub-basins including the Jhelum, Indus and Kabul River basins. Significantly increased high flows were observed in the glacier regime of UIB at Shigar and Shyok while decreased flows were predominant in Hunza River at Daniyor Bridge. A similar proclivity of predominantly reduced flows was observed in nival and rainfall regimes in terms of significant negative trends in the Jhelum, Kunhar, Neelum and Poonch River basins. The timing of the high flows has not changed radically as magnitude at all gauging stations. For the low flows, decreasing significant trends were detected in the annual flows as well as in other extremes of low flows (1-day, 7-day, 15-day). The more profound and decreasing pattern of low flows was observed in summer at most of the gauging stations; however, such stations exhibited increased low flows in autumn, winter and spring. The decrease in low flows indicates the extension of dry periods particularly in summer. The high-water demand in summer will be compromised due to consistently reducing summer flows; the lower the water availability, the lower will be the crop yield and electricity generation.

Abstract Due to the lack of centralized identity management and the broadcast nature of wireless ad hoc networks, identity attacks are always tempting. The attackers can create multiple illegitimate (arbitrary or spoofed) identities on their physical devices for various malicious reasons, such as to launch Denial of Service attacks and to evade detection and accountability. In one scenario, the attacker creates more than one identity on a single physical device, which is called a Sybil attack. In the other one, the attacker creates cloned/replicated nodes. We refer collectively to these attacks as identity attacks. Using these malicious techniques, the attacker would perform activities in the network for which the attacker may not be authorized. In the existing literature, these attacks are often counteracted separately. However, in this paper, we propose a solution to counteract both attacks jointly. Our proposed scheme uses the received signal strength for the detection without using extra hardware (such as GPS, antennae or air monitors) and centralized entities (such as trusted third party or certification authority). Upon the detection of malicious identities, they will be quarantined and will be blacklisted for future data communication by the mobile nodes. Our proposed attack detector detects the presence of Sybil attacks and replication attacks locally by analysing the received signal strength captured by each node. Moreover, we propose a technique that will identify these attacks in the overall network. In both local and global cases, we evaluate our solutions theoretically and via simulation in NS-2. The obtained results demonstrate that it is possible to detect identity attacks with considerable accuracy without causing extra overhead in the form of extra hardware, periodic beacons or expensive localization operations in the wireless ad hoc networks.

Due to the lack of centralized identity management and the broadcast nature, wireless ad hoc networks are always a palatable target for masquerading attacks. The attackers can spoof identities of privileged legitimate users for various malicious reasons, such as to launch DoS or DDoS attacks, to access unauthorized information, and to evade the detection and accountability. In the current and limited literature, masquerading attacks are mostly counteracted by signal-strength-based detection systems. However, these schemes are mostly proposed to work for infrastructure-based IEEE 802.11 wireless networks using fixed access points, air monitors, or fixed anchor nodes, which are not suitable for the ad hoc architecture. In this paper, we propose a detection system for masquerading attacks without using fixed anchor nodes or air monitors. We develop an anomaly detection model based on the statistical significant testing for our masquerading detection system that takes into consideration the signal strength fluctuation. We conduct a test bed of Samsung Galaxy-based smartphones in order to analyze the real-world signal strength variation. We also plug the real-world signal strength variation in our model for the evaluation of the detection accuracy. We propose the received signal-strength-based masquerading attack detection scheme, which is carried out first by each node in its one-hop vicinity and then extended to five-hop proximity for broader detection scope and improved accuracy. The proposed scheme is evaluated using an NS-2 network simulator for detection accuracy in different environments. The results obtained indicate that our proposed scheme produces more than 90% true positives.

Internet of Vehicles (IoV) has emerged as an advancement over the traditional Vehicular Ad-hoc Networks (VANETs) towards achieving a more efficient intelligent transportation system that is capable of providing various intelligent services and supporting different applications for the drivers and passengers on roads. In order for the IoV and VANETs environments to be able to offer such beneficial road services, huge amounts of data are generated and exchanged among the different communicated entities in these vehicular networks wirelessly via open channels, which could attract the adversaries and threaten the network with several possible types of security attacks. In this survey, we target the authentication part of the security system while highlighting the efficiency of blockchains in the IoV and VANETs environments. First, a detailed background on IoV and blockchain is provided, followed by a wide range of security requirements, challenges, and possible attacks in vehicular networks. Then, a more focused review is provided on the recent blockchain-based authentication schemes in IoV and VANETs with a detailed comparative study in terms of techniques used, network models, evaluation tools, and attacks counteracted. Lastly, some future challenges for IoV security are discussed that are necessary to be addressed in the upcoming research.

Currently, the popularity of Internet-of-Vehicles technology and self-driving cars are increasing rapidly. Several companies are investing in this field and are competing to release the latest and safest autonomous cars. However, this rapid Internet-of-Vehicles development also creates many security problems, which are considered a significant threat both to industry and to consumers. As a result, there is an urgent need to study the possible security threats and different solutions that can ensure the safety of drivers and also the security of industry. This research article focuses on examining the systematic literature on Internet-of-Vehicles and security. It also provides comprehensive and unbiased information regarding various state-of-the-art security problems, solutions, and proposals in vehicular ad hoc networks and Internet-of-Vehicles. Systematic literature review is used for more than 127 different research articles published between the years 2010 and 2018. The results of the systematic literature review used are categorized into the following three main categories: (1) the different types of attacks on Internet-of-Vehicles, (2) the different solutions that can be implemented to solve the threats, and (3) the performance outcomes.

The economy of Pakistan relies on the agricultural sector which mainly depends on the irrigation water generating from the upper Indus river basin. Mangla watershed is a trans-boundary basin which shares borders of India and Pakistan, it comprises five major sub-basins, i.e., Jhelum, Poonch, Kanshi, Neelum and Kunhar. The runoff production of this basin is largely controlled by snowmelt in combination with the winter precipitation in the upper part of the basin and summer monsoon. The present study focusses on the application of a statistical downscaling method to generate future climatic scenarios of climatic trends (temperature and precipitation) in Mangla watershed. Statistical Downscaling Model (SDSM) was applied to downscale the Hadley Centre Coupled Model, version 3, Global Climate Model (HadCM3-GCM) predictions of the A2 and B2 emission scenarios. The surface water analyst tool (SWAT) hydrological model was used for the future projected streamflows based on developing climate change scenarios by SDSM. The results revealed an increasing trend of annual maximum temperature (A2) at the rates of 0.4, 0.7 and 1.2 °C for the periods of 2020s, 2050s and 2080s, respectively. However, a consistent decreasing trend of temperature was observed at the high-altitude region. Similarly, the annual minimum temperature exhibited an increasing pattern at the rates of 0.3, 0.5 and 0.9 °C for the periods of 2020s, 2050s and 2080s, respectively. Furthermore, similar increases were observed for annual precipitation at the rates of 6%, 10%, and 19% during 2020, 2050 and 2080, respectively, for the whole watershed. Significant increasing precipitation trends in the future (2080) were observed in Kunhar, Neelum, Poonch and Kanshi sub-basins at the rates of 16%, 11%, 13% and 59%, respectively. Consequently, increased annual streamflow in the future at the rate of 15% was observed attributing to an increased temperature for snow melting in Mangla watershed. The similar increasing streamflow trend is consistent with the seasonal trends in terms of winter (16%), spring (19%) and summer (20%); however, autumn exhibited decreasing trend for all periods.

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are deployed as Low power and Lossy Networks (LLN) to provide innovative services in various application domains such as smart cities, smart health, and smart communities. The LLN is a form of a network where the interconnected devices are highly resource-constrained (i.e., power, memory, and processing) and characterized by high loss rates, low data rates, and instability in the communication links. Additionally, IoT devices produce a massive amount of confidential and security-sensitive data. Various cryptographic-based techniques exist that can effectively cope with security attacks but are not suitable for IoT as they incur high consumption of resources (i.e., memory, storage and processing). One way to address this problem is by offloading the additional security-related operations to a more resourceful entity such as a fog-based node. Generally, fog computing enables security and analysis of latency-sensitive data directly at the network’s edge. This paper proposes a novel Fog Security Service (FSS) to provide end-to-end security at the fog layer for IoT devices using two well-established cryptographic schemes, identity-based encryption, and identity-based signature. The FSS provides security services such as authentication, confidentiality, and non-repudiation. The proposed architecture would be implemented and evaluated in an OPNET simulator using a single network topology with different traffic loads. The FSS performed better when compared with the APaaS and the legacy method.