Medable Inc., the leading technology provider for modern clinical trials, announces new intelligent automation technology applied across its clinical trials platform to cut standard trial build timelines by at least half. Early application of the technology in electronic clinical outcomes assessment (eCOA) deployments – a major delay to study startup industry-wide – is groundbreaking. By automating laborious, manual tasks such as testing, Medable saves substantial time and removes eCOA from the critical path to trial go-live.

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Medable’s New Intelligent Automation Capabilities Cut Clinical Trial Technology Deployment Timelines by 50% #clinicaltrials #ai #clinicalresearch #studystartup #biosciences #healthcare #drugdevelopment

Medable’s new AI and automation capabilities catapult the company to the forefront of automating clinical trial operations and align with the industry’s perennial need to launch studies faster. Now, sponsors can eliminate common gridlocks caused by conversion, configuration, validation, and quality engineering. Top-10 global pharmaceutical companies are already benefiting from Medable’s novel technology, shaving weeks off build times.

As part of a broader roadmap incorporating AI and intelligent automation, Medable’s updated eCOA capabilities include an auto-configuration tool that produces standard configurations, such as schedules of assessments, anchor dates, and patient flags, in minutes. Medable’s auto-validate tool also eliminates weeks of tedious testing to automatically deliver a downloadable Configuration Validation Report (CVR) that validates the quality of study builds.

“We are reimagining the way clinical trials are deployed by eliminating many of the biggest process bottlenecks,” said Michelle Longmire, CEO and co-founder of Medable. “Starting with eCOA, we are accelerating clinical trials while helping to improve data quality – ultimately enabling our vision of a one-day study start-up to help deliver effective treatments and cures to patients faster. In 2024, expect to see more innovative technologies and new ways of delivering streamlined, patient-centric trials from Medable.”

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Medable, which ranked in the top 8% of software companies on the 2023 Inc. 5000, has deployed its software-as-a-service platform in more than 300 decentralized and hybrid clinical trials in 60 countries, serving more than one million patients and research participants globally. Customers have achieved impressive results – including 200 percent faster enrollment and 50 percent cost reductions. A Tufts Center for the Study of Drug Development study shows that, on average, decentralized trials can achieve net financial benefits from five to 13 times for Phase II and Phase III trials, equating to roughly $10 million ROI and $39 million ROI respectively.

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A report by Accenture states that AI adoption in the energy sector could result in a 20% increase in energy efficiency by 2035.

What Role Does AI Play in Reshaping the Energy Management Industry?

In recent years, AI has become an increasingly important technology in the energy and power industries. It can automate and optimize a variety of energy-related activities, leading to more efficient and less expensive operations, improved energy management, and less adverse environmental impacts. Demand forecasting is one of the most important areas where AI is being employed in the energy business. Utility businesses may improve resource allocation and management with the help of AI systems that can more precisely predict energy usage by analyzing data on consumer behavior, weather patterns, and other variables.

With the help of AI, the generation and distribution of energy might be optimized.

Machine learning algorithms, for instance, may analyze data from solar or wind power plants to identify patterns and make predictions about future energy production. The sometimes erratic output of renewable energy sources may be more manageable for operators. One of the most important applications of AI in the energy industry is in the field of building energy management. Artificial intelligence-enabled devices may monitor and assess a building’s energy use, identifying wasteful practices and providing recommendations for improvement. This has the potential to save building owners and occupants a lot of money while also reducing their carbon footprint.

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Energy Intelligence

Utilities may benefit from artificial intelligence (AI) capabilities like machine learning, natural language processing, and computer vision in a variety of ways, including increased accuracy in demand forecasting, more efficient energy generation and distribution, and faster troubleshooting of malfunctioning machinery. The efficiency and quality of services provided by a facility can be improved while costs are reduced.

Since utilities are under increasing pressure to optimize energy production and distribution to meet rising demand while also ensuring that their systems remain reliable and cost-effective, the market for AI in the energy and power industry has benefited.

The U.S. Energy Information Administration (EIA) estimates that global energy consumption will increase by over 50% between 2018 and 2050.

By improving the grid’s capacity to incorporate renewable energy sources and controlling energy storage and distribution, artificial intelligence can help alleviate a variety of problems related to renewable energy use. This has the potential to improve the power system’s dependability and stability while also decreasing the cost and increasing the sustainability of energy generation.

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Top Companies in the Energy Sector

• Alpiq
• SmartCloud Inc.
• General Electric
• Siemens AG
• Hazama Ando Corporation
• ATOS SE
• AppOrchid Inc.
• Zen Robotics Ltd.
• Origami Energy Ltd.
• Flex Ltd.

Top 10 Trends of AI in the Energy Sector

Smart Grids

A smart grid is an idea made possible by the use of AI in energy management. To maximize efficiency in power generation, transmission, and use a “smart grid” incorporates existing power infrastructure with cutting-edge technology such as artificial intelligence.

The International Energy Agency (IEA) estimates that smart grids with AI applications may cut power consumption by 10 percent and greenhouse gas emissions by 15 percent.

AI algorithms can evaluate real-time data from smart meters, sensors, and IoT devices to discover abnormalities, forecast equipment breakdowns, and optimize energy flow. AI helps utilities find that sweet spot between supply and demand by smartly regulating energy distribution. Less energy wasted and a marked improvement in the effectiveness of the grid as a whole. Artificial intelligence is about to have a profound effect on the energy management industry.

Microgrids

Microgrids are smaller versions of electricity grids that may function autonomously from the larger, more central grid. Artificial intelligence and machine learning are utilized by microgrid control systems to regulate energy flow and maximize efficiency. Microgrids are gaining popularity due to their ability to integrate renewable energy sources into the energy grid and offer backup power in the event of an outage.

Detecting Energy Theft and Fraud

As much as $6 billion is lost annually in the United States due to electricity theft and fraud in the energy and utilities sector.

Energy theft occurs when someone illegally takes power from the grid. Misrepresenting energy statistics or use is considered energy fraud. Automated anomaly detection with AI and ML can alert utilities to potential problems. In doing so, energy providers may safeguard their assets, cut down on unnecessary energy use, and pocket the savings.

Grid Management Energy Efficiency and Demand Response

Sustainable energy management relies heavily on improving energy efficiency, and AI is crucial in this regard. Artificial intelligence systems can analyze consumption habits and construct energy models to pinpoint inefficiencies and provide solutions to cut down on waste.

Artificial intelligence makes possible demand response schemes that pay people to reduce their energy use during high-demand times. Consumers may help alleviate grid congestion and support a cleaner energy environment by using AI-enabled smart devices and home automation systems to engage in demand response efforts.

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Energy Trading

Due to the time-sensitive nature of energy delivery, trading in energy is distinct from trading in other commodities. For energy dealers, this poses a difficulty but also an opportunity since the energy market is becoming more liquid. Predicting energy demand and giving traders real-time information about energy pricing are two ways in which AI and machine learning might improve the efficiency of the energy trading market.

Energy brokers can use this data to better time their purchases and sales of energy. Power purchase agreements (PPAs) are a new kind of financial contract that may be executed on the blockchain. The adoption of blockchain technology improves the effectiveness of these contracts since it speeds up transactions, lowers associated costs compared to more conventional PPA platforms, and is built on a more robust and reliable infrastructure.

Grid Safety

Because of its complexity, the electricity infrastructure is susceptible to cyberattacks.

By thwarting cyberattacks in advance, AI and machine learning can make power systems safer for everyone. Data analytics is used to look for indicators of a cyberattack in energy usage data. Artificial intelligence and machine learning can be used to counteract cyberattacks once they have been detected.

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Predictive Analytics

AI’s use of predictive analytics is a significant addition to the field of energy management. Predicting energy consumption patterns, weather conditions, and equipment performance are all areas where AI systems thrive through the analysis of massive amounts of historical and real-time data.

A recent report predicts that global AI in the energy sector is expected to reach $7.78 billion by 2025, driven by the increasing adoption of predictive analytics.

For instance, utilities may improve electricity generation and distribution by using AI algorithms to forecast peak energy demand. In addition to saving money, this improves the reliability of the power grid. AI aids energy suppliers in making wise choices and optimizing resource allocation through precise predictions of energy usage.

Customer Engagement

AI and ML are being put to use for the first time in the energy industry to improve interaction with customers. Companies in the energy industry may better serve their consumers’ demands by applying AI and machine learning. Data analytics are used to learn about customers’ energy consumption patterns, and those patterns are then used to tell consumers about how they might cut their energy use through behavioral changes.

Boosted Output

The energy industry is likewise making use of AI and ML to boost output. Machine learning algorithms are being used by the oil and gas industry, for instance, to optimize well location and boost output. Companies may make more informed judgments about where to drill for oil and gas by analyzing data gathered from seismic surveys and other sources. This will improve energy efficiency while also making the electricity grid simpler and more efficient.

Energy-Storage Devices

By 2030, the energy storage industry is expected to have expanded by a factor of 20. Integrating smart energy storage devices into the electric grid is a step toward more effective energy management. Virtual power plants, which are made possible by energy storage and allow utilities to meet peak demand even when supplies are low, are another example of this trend. As a result, fewer new power plants will need to be constructed by the energy industry.

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One may make the case that creators, authors, and artists should have some say over who uses and profits from their work. This is typically accomplished via copyright laws. In most cases, the legal notion of “individual intellectual effort” is used by these statutes to establish authorship. In other words, the artist must have infused their work with sufficient originality and imagination to set it apart from previous works. But how can a person accomplish this? Some contend that, in contrast to AI, humans possess a unique quality that enables us to produce “new” works of art.

The IP battle between humans and AI

When it comes to intellectual property law, many jurisdictions have ruled that only “real humans” can be inventors, creators, or authors. However, when AI is involved, it’s not always apparent who is regarded as the author of a piece. Currently popular generative AI solutions take text suggestions as input and output what the user wants. Did a human put in enough labor to be deemed the author, inventor, or creator of the output work when they entered a specific set of prompts into an AI tool? In such case, where did the creative energy and originality originate from if the work is not plagiarized?

Many issues arise for those making and utilizing these technologies as a result of this line of thinking, particularly when trying to establish ownership. Generally speaking, it’s bad for the IP system as a whole. What, however, happens when an AI tool reaches the point where it is as knowledgeable as a human and has accumulated all the facts and experiences that a human could ever have? Similar to how a chess computer can anticipate every possible move a grandmaster would make, the AI would be capable of solving every difficulty that a person might think of. As a result, practically no new ideas are generated today, unless the human creator possesses exclusive, non-disclosable data.

Avoiding intellectual property problems with generative AI

You can take immediate, actionable actions to guarantee that anything created with the aid of generative AI will be credited to you as the creator, author, or inventor. The most critical thing is to keep track of when and how you employ AI technologies, as well as the data you use to obtain results. The newest generation of AI tools require you to document the prompts you use, together with the date and version of the tool, so they can be properly tracked. This might be very important later on when you need to prove that you were the rightful creator or inventor by demonstrating that enough “intellectual effort” was put into it.

It is important to ensure that you possess adequate rights to the datasets utilized for training new AI tools before beginning development. By doing so, you may rest assured that your tool’s underlying AI model will not mistakenly generate derivative works that violate the rights of others. The number of governments mandating the sharing of training datasets is expected to grow over time.

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What AI can’t do is replace the natural ‘gut feel’ of a healthcare professional.

Smart Use of Artificial Intelligence in Health Care- How Robots are Helping Doctors?

With the help of AI, telemedicine and RPM may provide patients with up-to-the-minute health information. To guarantee ethical AI use, legislative frameworks, and ethical concerns have come to the fore. Instead of seeing AI as a replacement for human healthcare providers, the focus is on how AI systems may work in tandem with them. The picture highlights the revolutionary integration of AI into several aspects of healthcare, which holds great promise for better diagnoses, personalized treatments, and overall patient care.

By 2024, artificial intelligence is expected to make great strides in the medical field. Deep learning algorithms improve the accuracy of X-ray, MRI, and CT scan interpretation, which is useful in medical imaging and diagnostics. AI plays a crucial role in the drug discovery process by sifting through large datasets in search of promising compounds and automating development. Using a patient’s unique genetic and molecular profile, personalized medicine employs AI to create individualized treatment plans. Using natural language processing to glean insights from unstructured clinical data, predictive analytics helps with patient outcome prediction and at-risk population management.

The research examines six key areas where AI directly affects the patient and three sectors of the healthcare value chain that might gain from more scaling of AI. It also looks at specific instances of current AI solutions in healthcare.

The increase in investments isn’t surprising, as 90% of the healthcare leaders surveyed believe that AI initiatives are important for their organizations to remain competitive in the market. When asked about their organization’s approach to technology innovation, 80% self-reported that they are either edge experimenters (organizations that tend to be first adopters of new technology or first to try new approaches and test unknown use cases) or fast followers (organizations that typically are next in line to adopt after some experimentation).

Robots Cannot Substitute Doctors!

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Unitree Robotics released the new industrial quadruped robot B2.As a leading force in the industry, Unitree industrial quadruped robot brought by Unitree has been upgraded on the basis of the B1 with a full range of upgrades, including the comprehensive capacity of load, range, motion capability, speed, etc., enabling it to play a role in more application scenarios.

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1. The fastest running industrial-grade quadruped robot
B2 has a significant improvement in speed, with a running speed of more than 6m/s to become the fastest known industrial-grade quadruped robot on the market. At the same time, it has a maximum jumping distance of up to 1.6m.

2. Continuous load increased by 100%, endurance soared by 200
B2’s maximum standing load capacity reaches 120kg, continuous walking load is more than 40kg with an increase of 100%; 20kg load continuous walking is more than 4 hours with more than 15km mileage, and endurance is increased by 200%; unloaded continuous walking endurance is more than 5 hours with more than 20km mileage. This means that B2 is able to carry heavy loads, perform delivery tasks or work continuously for long periods of time.

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3. Powerful joint performance increased by 170% with 360N.m strong torque
With a peak torque of 360N.m, a 170% increase in performance, B2 providing greater flexibility and stability for industrial operations.

4. Stable and strong, all-round to deal with diverse environments
B2 has superb obstacle-crossing ability and can easily cross various obstacles, providing an excellent solution for dealing with complex environments. In addition, B2 also has all-terrain adaptability, and can walk stably in slippery or extreme terrain.

5. Depth perception to cope with various complex challenges
In terms of perception capability, B2 can be paired with a variety of sensors, including 3D LIDAR, depth cameras and optical cameras, which enable Unitree robot to adapt to its surroundings better, thus accomplishing various tasks more safely and efficiently.

Unitree said that B2 industrial quadruped robot will be widely used in various industries, such as industrial automation, power inspection, emergency rescue, industrial inspection, education and research. Its high performance and versatility will enable it to play an important role in these fields, improving work efficiency, reducing labor costs, and lowering risks and dangers.

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How Can AI Fit Into the Field of Neonatology?

Preterm births account for about 10% of live births, and almost all preterm newborns have trouble feeding. The number of newborns in the United States who experience difficulty feeding is increasing, and it currently stands at over 2.8 million.

In neonatology, artificial intelligence is currently being tested for a variety of purposes, such as monitoring vital signs, diagnosing and prognosing neurological disorders, and predicting diseases such as respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity, retinopathy of prematurity, intestinal perforation, and jaundice.

A Review of NICU Data Quality

Think about a typical neonatal intensive care unit (NICU): the tempo of care is high, and practically all clinical decisions are made in real-time. The lack of high-quality data is already making those judgments difficult. Despite the wealth of clinical data stored in EHRs, the unstructured data that NICU teams require is frequently obscured, absent, or filled with errors in patient records.

A NICU nurse, for example, can probably tell if a dosage of medication has been inadvertently increased by zero based on a baby’s weight and can immediately account for the mistake because the human eye can fix many of these mistakes. That AI will act similarly is still a mystery to humans. Already, clinicians’ mistrust and the excessive amount of rework caused by poor data quality in NICUs are out of control.

It’s adding fuel to the fire of burnout among nurses who feel a moral imperative to verify every detail in a patient’s chart, even if they rarely have the opportunity to do so. Assuming the AI systems are indeed developed using pediatric data, NICUs are not in a position to responsibly rely on them for any part of patient care.

There is a dearth of pediatric data in current AI research, which causes researchers to draw incorrect conclusions about pediatric populations from adult datasets, as pointed out in a new framework of guidelines for the responsible use of pediatric data in AI studies. It is often believed that NICUs should immediately begin using AI to improve patient outcomes and decision-making. However, health systems would be better served by exercising prudence and bolstering the quality of their clinical data in NICUs. You get out of life what you put into it, as the adage goes.

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The United Nations Internet Governance Forum (IGF) Dynamic Coalition on Blockchain Assurance & Standardization announces the initiation of a groundbreaking project aimed at establishing a Distributed Autonomous Organization (DAO). The pilot project is a collaborative effort showcasing how public sector organizations can leverage blockchain technology and DAO principles to foster transparent, rules-based, and high-integrity governance structures.

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UN – IGF Dynamic Coalitions are multistakeholder groups operating within the framework of the UN Internet Governance Forum, addressing specific issues related to internet governance. These coalitions are independent entities within the UN-IGF and bring together diverse stakeholders to collaborate on focused topics, fostering dialogue and producing outputs such as reports and guidelines.

A Distributed Autonomous Organization (DAO) is an organization governed by a set of rules encoded as smart contracts on a blockchain, operating on principles of decentralization and automation. In a DAO, decision-making and governance processes are encoded into smart contracts on a blockchain, enabling members to participate in the organization’s activities without the need for traditional hierarchical structures. DAOs typically utilize digital tokens for voting and decision-making, and their rules are transparently and immutably recorded on the blockchain. This decentralized approach aims to create trust, transparency, and efficiency, allowing members to collectively manage resources, make decisions, and execute actions based on predefined rules without the need for centralized control.

According to one of the groups co-leaders, Mr. Dino Cataldo Dell’Accio who also is the CIO of the United Nations Joint Staff Pension Fund, “This initiative signifies a pivotal step toward establishing innovative and secure governance models, ensuring that blockchain technology can be harnessed for the benefit of public sector organizations. The UN IGF DC-BAS remains committed to fostering collaboration, transparency, and the responsible adoption of emerging technologies.”

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The Dynamic Coalition is hosted by the Government Blockchain Association (GBA), a non-profit that helps the public and private sector connect, communicate, and collaborate. The GBA provides the infrastructure support for the organization. The Dynamic Coalition is also co-led by the GBA Executive Director, Gerard Dache and along with Mr. Dino Cataldo Dell’Accio, they lead a public, open forum multi-stakeholder coalition from around the world that meets on a monthly basis.

The technology being used for this pilot project is donated by a GBA member, Gosh, an Ethereum Layer 2 solution that allows developers to run smart contracts on-chain for free. The pilot will also liaise with other GBA working groups, including:

• Application Architecture
• Digital Asset Management
• DAO Governance
• Legal & Legislative

Anyone can join the group by requesting access to the UN IGF BAS-DC Google Group. For more information about the Dynamic Coalition.

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Varonis’ customers benefit from intelligent automation to strengthen data security posture and detect advanced threats

Varonis Systems, announced that it has launched its Data Security Platform on Salesforce AppExchange, a leading enterprise marketplace for partner apps and experts. Varonis empowers organizations to secure critical customer data in the world’s number-one CRM by providing deep visibility, threat detection, and incident response capabilities.

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Varonis Data Security Platform

While Salesforce employs security by design, end-user organizations are responsible for keeping their own data safe from cyberattacks. Varonis enhances Salesforce Shield, the CRM leader’s native solution for comprehensive security for critical data — providing powerful threat detection and prevention, data encryption, and investigation capabilities that help ensure sensitive data remains secure and compliant with data privacy regulations. Organizations can now easily request a demo of Varonis or a Data Risk Assessment right on Salesforce AppExchange.

Comments on the News

• “At Varonis, we’re thrilled to be teaming up with Salesforce to provide organizations with tools and knowledge to protect their most valuable asset — customer data,” said David Bass, Executive Vice President of Engineering and Chief Technology Officer, Varonis. “By joining forces, we’re helping customers rest assured knowing that their mission-critical information is secure from cyber threats.”
• “Varonis’ Data Security Platform is a welcome addition to AppExchange, as it accelerates business transformation for customers by keeping data safe, so organizations can realize the full potential of their CRM data — securely,” said Alice Steinglass, Executive Vice President and General Manager, Platform. “AppExchange is constantly evolving to connect customers with the right apps and experts for their business needs.”

Salesforce AppExchange, a leading enterprise marketplace for partner apps and experts, empowers companies, developers, and entrepreneurs to build, market, and grow in entirely new ways. Since its launch in 2006, the platform has grown to include more than 8,000 apps and experts, with over 12 million customer installs. AppExchange connects customers of all sizes and across industries to ready-to-install or customizable apps and Salesforce-certified consultants to solve any business challenge.

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Salesforce, AppExchange, Salesforce Shield and others are among the trademarks of salesforce.com, inc.

Varonis  is a leader in data security, fighting a different battle than conventional cybersecurity companies. Our cloud-native Data Security Platform continuously discovers and classifies critical data, removes exposures, and detects advanced threats with AI-powered automation.

Thousands of organizations worldwide trust Varonis to defend their data wherever it lives — across SaaS, IaaS, and hybrid cloud environments. Customers use Varonis to automate a wide range of security outcomes, including data security posture management (DSPM), data classification, data access governance (DAG), data detection and response (DDR), data loss prevention (DLP), and insider risk management.

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[To share your insights with us, please write to sghosh@martechseries.com]

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Deal Accelerates watsonx Data Ingestion Capabilities; Creates a Leading Application Modernization and Integration Platform

€2.13 billion Acquisition Demonstrates IBM’s Continued Focus on Strategic M&A to Create Value and Bolster AI and Hybrid Cloud Offerings

IBM announced that it has entered into a definitive agreement with Software AG, a company majority owned by Silver Lake, to purchase StreamSets and webMethods, Software AG’s Super iPaaS (integration platform-as-a-service) enterprise technology platforms, for €2.13 billion in cash.

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As organizations continue to accelerate their digital transformations, application and data integration solutions are critical elements for application modernization and effectively deploying AI across the enterprise. StreamSets and webMethods are among the technology leaders in application integration, API management, and data integration. IDC predicts the worldwide integration software market will exceed $18.0 billion in 2027 at a compound annual growth rate (CAGR) of 16.1%.

The acquisition of StreamSets and webMethods is further evidence of IBM’s deep focus and investment in AI and hybrid cloud. StreamSets will add data ingestion capabilities to watsonx, IBM’s AI and data platform, while webMethods will give clients and partners additional integration and API management tools for their hybrid multi-cloud environments.

The core offerings include:

• StreamSets – a cloud-native DataOps and data ingestion platform which allows enterprises to achieve consistent access and delivery of data across a wide spectrum of data sources and types. StreamSets also facilitates the design of smart data pipelines and the ingestion of real-time and batch data.
  
  
• webMethods – an integration and API management platform. The integration platform, deployed both on-prem and in the cloud, offers B2B integration, managed file transfer, and provides a modern API gateway to manage, monitor, and monetize APIs.

Software AG’s Super iPaaS platform is growing, profitable and has a significant recurring revenue profile. StreamSets and webMethods serve more than 1,500 clients across the globe.

“Together with IBM’s watsonx AI and data platform, as well as its application modernization, data fabric and IT automation products, StreamSets and webMethods will help clients unlock the full potential of their applications and data. This powerful combination helps drive innovation while preparing businesses for AI, no matter where applications or data reside,” said Rob Thomas, Senior Vice President, Software and Chief Commercial Officer, IBM. “Today’s news will give IBM clients and partners one of the most modern and comprehensive application and data integration platforms in the industry.”

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The pending acquisition follows a more than 20-year relationship between IBM and Software AG, with both StreamSets and webMethods expected to drive synergy with IBM’s current portfolio, including watsonx, Red Hat, the company’s IT automation products, as well as IBM Consulting. Additionally, StreamSets and webMethods are expected to benefit from IBM’s global scale, operating in more than 175 countries.

“IBM is the ideal home for webMethods and StreamSets, the products at the heart of our Super iPaaS vision,” said Sanjay Brahmawar, Chief Executive Officer of Software AG. “Combined with IBM’s global scale and focus on hybrid cloud and AI, our people will have a fantastic opportunity to develop while helping enterprises everywhere get the most out of their applications and data.”

“We believe that there is no business more iconic or better suited than IBM to continue investing in and growing these great platforms,” said Christian Lucas, Chairman of the Supervisory Board of Software AG and Managing Partner of Silver Lake. “The opportunity to bring the StreamSets and webMethods teams together with IBM to innovate in building the future of hybrid cloud and next-generation AI solutions for the enterprise is uniquely compelling. We are pleased that our investment in Software AG helped spark that vision, reflecting Silver Lake’s commitment to partner with the world’s best management teams and technology leaders in driving exceptional value creation for customers, employees and other stakeholders.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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