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In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Nickel-Titanium (Ni-Ti) thin films have gained a lot of attention due to their unique features, such as the shape memory effect. Micro-actuators, micro-valve, micro-fluid pumps, bio-medical applications, and electronic applications have a lot of interest in these smart thin films. Sputter-deposited NiTi thin films have shown the potential to be very useful as a powerful actuator in micro-electro-mechanical systems (MEMS) because of their large recovery forces and high recoverable strains. Despite the advancement of improved deposition methods for the NiTi thin films, there are still certain unsolved challenges that impede accurate composition control throughout the deposition process. Many applications, spanning from the aerospace industries to a range of nanotechnologies, require knowledge of the sputtering characteristics of the materials that are subjected to bombardment, ejection, and deposition of ions. In recent decades, atomic scale modeling has been given a high emphasis in ion sputtering research, providing an adequate and precise description of collision cascades in solids using the Stopping and Range of Ions in Matter (SRIM) and Transport of Ions in Matter (TRIM). In this paper, SRIM is used to address how the heavy ions interact with the target materials. A variety of ion-solid interaction characteristics, including the sputter yield, have been determined by simulating collision cascades in the solids. On the other hand, TRIM was used to describe the range of ions that enter into the matter and cause damage to the target throughout the process. The simulation was carried out to compare the sputtering yield of Ni and Ti by varying the energy input (from 300V to 1300V). SRIM simulation was conducted by varying the thickness of the film, the angle of incidence of ions, and the energy involved in the sputtering process. The characterization of the films has been carried out using Field Emission Scanning Electron Microscopy (FESEM) to comprehend the surface and interface morphologies of the films and to validate the simulated results. With an increase in energy input (target voltage), the sputtering yield increased. The sputtering yield of the Ni target was higher than the Ti target indicating that Ni can be removed relatively easier than Ti.

Due to its large leakage power and low density, the conventional SARM becomes less appealing to implement the large on-chip cache due to energy issue. Emerging non-volatile memory technologies, such as phase change memory (PCM) and spin-transfer torque RAM (STT-RAM), have advantages of low leakage power and high density, which makes them good candidates for on-chip cache. In particular, STT-RAM has longer endurance and shorter access latency over PCM. There are two kinds of STT-RAM so far: single-level cell (SLC) STT-RAM and multi-level cell (MLC) STT-RAM. Compared to the SLC STT-RAM, the MLC STT-RAM has higher density and lower leakage power, which makes it a even more promising candidate for future on-chip cache. However, MLC STT-RAM improves density at the cost of almost doubled write latency and energy compared to the SLC STT-RAM. These drawbacks degrade the system performance and diminish the energy benefits. To alleviate these problems, we propose a novel cache organization, companion write cache (CWC), which is a small fully associative SRAM cache, working with the main MLC STT-RAM cache in a master-and-servant way. The key function of CWC is to absorb the energy-consuming write updates from the MLC STT-RAM cache. The experimental results are promising that CWC can greatly reduce the write energy and dynamic energy, improve the performance and endurance of MLC STT-RAM cache compared to a baseline.

Moore’s law has been one of the reason behind the evolution of multicore architectures. Modern multicore architectures offer great amount of parallelism and on-chip resources that remain underutilized. This is partly due to inefficient resource allocation by operating system or application being executed. Consequently the poor resource utilization results in greater energy consumption and less throughput. This paper presents a fuzzy logic-based design space exploration (DSE) approach to reconfigure a multicore architecture according to workload requirements. The target design space is explored for L1 and L2 cache size and associativity, operating frequency, and number of cores, while the impact of various configurations of these parameters is analyzed on throughput, miss ratios for L1 and L2 cache and energy consumption. MARSSx86, a cycle accurate simulator, running various SPALSH-2 benchmark applications has been used to evaluate the architecture. The proposed fuzzy logic-based DSE approach resulted in reduction in energy consumption along with an overall improved throughput of the system.

This paper presents a method to characterize, identify and classify some pathological Electroencephalogram (EEG) signals. We use some Time Frequency Distributions (TFDs) to analyze its nonstationarity. The analysis is conducted by the spectrogram (SP), the Choi–Williams Distribution (CWD) and the Smoothed Pseudo Wigner Ville Distribution (SPWVD). The studies are carried on some real EEG signals collected from a known database. The estimation of the best value of parameters for each distribution is achieved using the Rényi entropy (RE). The time-frequency results have permitted to characterize some pathological EEG signals. In addition, the Rényi Marginal Entropy (RME) is used for the purpose of detecting the peak seizures and discriminates between normal and pathological EEG signals. The frequency bands are evaluated using the Marginal Frequency (MF). The EEG signal classification of two sets A and E containing normal and pathologic EEG signals, respectively, is performed using our proposed method based on energy extraction of signals from time-frequency plane. Also, the Moving Average (MA) is used as a tool to obtain better classification results. The results conducted on real-life EEG signals illustrate the effectiveness of the proposed method.

The rebel of global networked resource is Cloud computing and it shared the data to the users easily. With the widespread availability of network technologies, the user requests increase day by day. Nowadays, the foremost complication in Cloud technology is task scheduling. The cargo position and arrangement of the tasks are the two important parameters in the Cloud domain, which can provide the Quality of Service (QoS). In this paper, we formulated the optimal minimization of makespan and energy consumption in task scheduling using Local Pollination-based Gray Wolf Optimizer (LPGWO) algorithm. In the hybrid concept, Gray Wolf Optimizer (GWO) algorithm and Flower Pollination Algorithm (FPA) are combined and used. In the presence of GWO, the best searching factor is used to increase the convergence speed and FPA is used to distribute the data to the next packet of candidate solution using local pollination concept. Chaotic mapping and OBL are used to provide a suitable initial candidate for task solutions. Therefore, the experiments delivered better task scheduling results in low and high heterogeneities of physical machines. Ultimately, the comparison with the simulation results had shown the minimum convergence speed of makespan and energy consumption.

In this paper, a monitoring technique based on the wireless sensor network is investigated. The sensor nodes used for monitoring are developed in a simulation environment. Accordingly, the structure and workflow of wireless sensor network nodes are designed. Time-division multiple access (TDMA) protocol has been chosen as the medium access technique to ensure that the designed technique operates in an energy-efficient manner and packet collisions are not experienced. Fading channels, i.e., no interference, Ricean and Rayleigh, are taken into consideration. Energy consumption is decreased with the help of ad-hoc communication of sensor nodes. Throughput performance for different wireless fading channels and energy consumption are evaluated. The simulation results show that the sensor network can quickly collect medium information and transmit data to the processing center in real time. Besides, the proposed technique suggests the usefulness of wireless sensor networks in the terrestrial areas.

Wireless Sensor Networks (WSNs) have come across several things which include collecting data, handling data and distribution for super visioning specific applications such as the services needed, managing anything that occurred naturally, etc. They totally rely on applications. Therefore, the WSNs are classified under major networks. This is very essential. It can be defined as a network of networks that helps in proper flow of data. The main characteristics of WSN include its continuous changes in topologies, connected nodes with several chips and tunic routing protocol. There should be the better utilization of the available resources so that its life span may exceed. There should be an effective usage of available assets to avoid the waste. In our research, we propose a hybrid approach, namely, the Power Control Tree-Based Cluster (PCTBC) to identify the Sybil attacks in WSNs. It employs several stages structured clustering of nodes based on position and identity verification. This approach is utilized for the usage of energy consumption, effectiveness of detecting Sybil attacks inside clusters. The main aspects put into consideration are the efficient routing protocol of the distance between the hops and the energy power remains, where the distance between the hops is being computed using the Received Signal Strength Indication (RSSI) and also the packet transmission can be properly tuned on the basis of the distance. Also, the proposed approach considers the energy consumption for the transmission of the defined packet.

The emergence of cloud computing in big data era has exerted a substantial impact on our daily lives. The conventional reliability-aware workflow scheduling (RWS) is capable of improving or maintaining system reliability by fault tolerance techniques such as replication and checkpointing based recovery. However, the fault tolerant techniques used in RWS would inevitably result in higher system energy consumption, longer execution time, and worse thermal profiles that would in turn lead to a decreased hardware lifespan. To mitigate the lifetime-energy-makespan issues of RWS in cloud computing systems for big data, we propose a novel methodology that decomposes the complicated studied problem. In this methodology, we provide three procedures to solve the energy consumption, execution makespan, and hardware lifespan issues in cloud systems executing real-time workflow applications. We implement numerous simulation experiments to validate the proposed methodology for RWS. Simulation results clearly show that the proposed RWS strategies outperform comparative approaches in reducing energy consumption, shortening execution makespan, and prolonging system lifespan while maintaining high reliability. The improvements on energy saving, reduction on makespan, and increase in lifespan can be up to 23.8%, 18.6%, and 69.2%, respectively. Results also show the potentiality of the proposed method to develop a distributed analysis system for big data that serves satellite signal processing, earthquake early warning, and so on.

A prerequisite to ensure the stability of the power supply system is suitable functioning of transmission line equipment. However, the increasing deployment of transmission lines in modern power systems has introduced significant challenges to line inspection. While deep learning-based image detection techniques have shown promise in improving the efficiency and accuracy of insulator detection, they often require substantial computational resources and energy. This limitation hinders the consistent guarantee of accuracy and real-time performance on resource-constrained drones. To address this issue, this paper investigates the co-optimization problem of energy consumption and analytic accuracy in insulator image detection on unmanned aerial vehicles (UAVs). We propose a latency-aware end-edge cooperative insulator detection task offloading scheme with high energy efficiency and accuracy that aims to achieve optimal performance. Initially, we conducted an experimental analysis to examine the influence of input image resolution on the accuracy and latency of the CNN-based insulation detection model. Subsequently, we develop a model that takes into account the latency, analytic accuracy and energy consumption for image detection task offloading. Finally, we formalized a nonlinear integer optimization problem and designed a particle swarm optimization (PSO)-based task offloading scheme to optimize task accuracy and energy consumption while adhering to latency constraints. Extensive experiments validated the effectiveness of the proposed end-edge cooperative insulator detection method in optimizing accuracy and energy consumption.

The aluminum-based composites (AMCs) are known for a variety of functions like building, aerospace, automotive, marine, and aeronautical applications. In this research, Al-4032 alloy-based 6% SiC (by weight) composite has been fabricated using stir casting and the effects of prominent machining parameters on energy consumption and surface finish have been examined using carbide inserts in turning. Microstructures of as-cast specimens has been analyzed using the optical microscope, scanning electron microscopy, and energy-dispersive spectroscopy. The CNC turning has been performed at varying machining parameters like cutting speed, feed rate, and depth of cut, following an RSM-based design matrix. The desirability function approach has been employed to obtain the best combination of parameters for achieving the desired objectives. The experimental outcome demonstrates that the machined composite is considerably influenced by built-up edge (BUE) formation and interfacial bonding of particles. The result establishes that the inclusion of SiC in the Al-4032 matrix demonstrates improved mechanical properties and superior machined surface with the optimized turning operation.

Water Matters Engineering the Reclamation of H₂0.

Identification of Microbial Contamination in Water Treatment and Distribution Systems.

From Wastewater to Safe Water: The NTU Experience.

Second Generation Biofuels: The Way Forward.

INDIA – A novel form of gene regulation in bacteria.

INDIA – Algal biofuels are no energy panacea.

JAPAN – Medical Data Vision enhances the quality of medical care with Actian Vectorwise.

SINGAPORE – Singapore heart surgeon to receive honour from The Royal College of Surgeons of Edinburgh.

SINGAPORE – ELGA^® to deliver innovative water purification at new Singapore General Hospital expansion.

AUSTRALIA – Specialised Therapeutics Australia: New drug to fight hospital superbug infection.

AUSTRALIA – Group of genes hold the clue in migraine cases.

AUSTRALIA – CT scans can triple risk of brain cancer, leukemia.

BRAZIL – Science can do more for sustainable development.

MIDDLE EAST – Particles and persecution: why we should care about Iranian physicists.

EUROPE – Medicyte coordinates EU-funded collaboration on Biomimetic Bioartificial Liver.

EUROPE – Selvita and Orion Pharma achieve a research milestone in Alzheimer's Disease Program.

EUROPE – Zinforo (ceftaroline fosamil) receives positive CHMP opinion in the European Union for the treatment of patients with serious skin infections or community acquired pneumonia.

USA – Vein grown from girl's own stem cells transplanted.

USA – Hidden vitamin in milk yields remarkable health benefits - Weill Cornell researchers show tiny vitamin in milk, in high doses, makes mice leaner, faster and stronger.

USA – New report finds biotechnology companies are participating in 39% of all projects in development for new medicines and technologies for neglected diseases.

USA – TriReme Medical receives FDA clearance for expanded matrix of sizes of Chocolate PTA balloon catheter.

USA – New data show investigational compound dapagliflozin demonstrated significant reductions in blood sugar levels when added to sitagliptin in adults with type 2 diabetes at 24 weeks, with results maintained over 48 weeks.

USA – Zalicus successfully completes Phase 1 single ascending dose study with Z944, a novel, oral T-Type Calcium Channel Blocker.

USA – Study provides clues to clinical trial cost savings.

German biotech innovator Altona Diagnostics launches BioNexus-certified regional hub in Malaysia: ADT Biotech Sdn Bhd.

FEI launches "Explore the Unseen" image contest in partnership with National Geographic.

BIO applauds Representative Kaptur's Energy Investment Act of 2012.

Cytori to utilize Sistemic's SistemQC™ to strengthen understanding of mechanisms & support design of Next-Generation Cell Therapies.

Bosch packaging technology and Sartorius Stedim Biotech introduce PreVAS.

CellCentric and ZoBio enter into partnership to develop lead compounds against epigenetic drug targets.

INDIA – Bioven starts BV-NSCLC-001 Phase III trial in NSCLC.

INDIA – Initiative in Chemical Biology and Therapeutics.

PHILLIPPINES – Asia–Pacific Analysis: The slow road to green energy.

SINGAPORE – Takeda progressing well in Asia with New Drug Applications.

SINGAPORE – NTU and University of Warwick boost brainpower in global neuroscience research.

THAILAND – Thai PhD. student awarded Monsanto's Beachell–Borlaug International Scholarship for rice improvement research.

EUROPE – Open access will change the world, if scientists want it to.

UNITED STATES & CANADA – Verisante places Aura Beta Units for safety, verification testing in B.C., Alberta and Ontario clinics.

UNITED STATES & CANADA – Life Technologies sets new worldwide standard for criminal forensic testing with introduction of GlobalFilerTM Express Kit.

UNITED STATES & CANADA – How immune cells can nudge nerves to regrow.

UNITED STATES & CANADA – Improved Genomic Target Selection Using IDT Oligos.

UNITED STATES & CANADA – US team uncover non-invasive method for diagnosing epilepsy.

BAYER HealthCare and Economic Development Board to foster translational and clinical research for diseases prevalent in Asia

Ludwig Cancer Research and University of Oxford launch Cancer Immunotherapy Spinout

OmniComm Systems® Announces Reseller Agreement with Tri-I Biotech Shanghai Inc. Market Leading Life Sciences Consultancy to Resell OmniComm’s TrialOne® eSource Solution

Resverlogix Closes License Agreement and Enters Into Definitive Stock Purchase Agreement with Hepalink

Immunovaccine and PharmAthene Sign Exclusive Worldwide License Agreement to Develop and Commercialize an Anthrax Vaccine Formulated in DepoVax™

Commonwealth Financing Authority Announces New Alternative, Clean Energy Investment

ContraFect Corporation Announces Issuance of Key Patent Covering Novel Lysin Technology

M Pharmaceutical Inc. Rebrands Itself to Better Reflect Growing Family of Biomedical Technologies

Sustainable Apparel Coalition Recognizes SCS Global Services as Approved Verifier for New Pilot Program

StemCell United Announces Plans for PO and Listing on ASX

Roche Diagnostics Strengthens Footprint in Asia Pacific

Next-Generation Large Molecule Affinity Purification Solution Improves Biotherapeutic, Disease Biomarker and Sports Anti-Doping Bioanalysis.

MR Solutions Brings New 3D, 1000X Magnification Imaging Modality to Preclinical Research.

Using Saltidin to Guard Against the Zika Virus.

Nagasaki University Hospital Boosts Patient Care and Safety with Zebra Technologies' Enterprise-Class TC55 Touch Computer.

CSIRO Uses Dell Supercomputer to Help Combat Post-Childbirth Complications in Women.

Philips Launches “World's Sleepiest Website” to Help Restless Sleepers Maximize Their Slumber.

Telstar Aeolus Proven to Consume Up to 35% Less Power than the Current Market Average.

The following topics are under this section:

• Could Jurassic Park become a Reality?
• Brightening the Future with Liquid Sunshine
• New Gene Therapy for Huntington’s Disease Proves Successful in Mice
• Fight or Flight?
• Distinguishing between Two Species of Red Pandas
• Freeze-drying Soil for Studying Reactive Nitrogen Gas Emissions
• Remodelling the Future of Air Pollution Studies

The following topics are under this section:

• ASIA-PACIFIC — Discovery of Protease Inhibitors hold Promise against COVID-19
• ASIA-PACIFIC — Nano-sensors for Real-Time Plant Health Monitoring
• ASIA-PACIFIC — Study Finds Underweight Patients with Diabetes have Increased Risk of Tuberculosis
• ASIA-PACIFIC — Guide Developed for Remote Consultation for Suspected COVID-19 Patients
• ASIA-PACIFIC — Scientific Challenges of Kimchi Fermentation
• ASIA-PACIFIC — Increasing Charge Capacity of Lithium Batteries
• ASIA-PACIFIC — Newly-discovered Small Mitochondrial Protein Essential for Energy Production
• ASIA-PACIFIC — Overcoming Chemotherapy Resistance in Ovarian Cancer
• REST OF THE WORLD — Safety of Antibiotics for C-Section Mothers
• REST OF THE WORLD — Early Screening of Colorectal Cancer based on Family History
• REST OF THE WORLD — Maternal Hypertensive Disorders may cause Mental Health Disorders in Children
• REST OF THE WORLD — Biofilm Formation and Hyper-infectivity of Cholera

The routing information is hard to maintain and the energy is limited in highly dynamic wireless sensor network. To solve these problems, energy-saving geographic routing (ESGR) is proposed, which does not maintain the network topology and can save energy. A node broadcast its position information to its neighboring nodes before transmitting data. The neighboring nodes compute the position of the virtual relay node using the data transmitter position, the base station position and the energy consumption for circuits and propagation. The neighboring nodes determine whether to become the relay node through competition based on its position, the destination position and the virtual relay node position. The neighboring nodes compute the delay time distributedly according to the competition strategy. The neighboring node with the shortest delay time can respond to the data sender first and become the sole relay node. The handshake mechanism efficiently prevents the collision among the neighboring nodes during competition, which is of high communication efficiency. When a routing hole is found, the relay region is changed and an approaching destination relay strategy is adopted, which reduces the impact of routing holes. The simulation shows that the proposed algorithm is better than BLR, because of the lower energy consumption and lower packet loss ratio. The ESGR algorithm is more appropriate for highly dynamic wireless network.