Lappeenranta-Lahti University of Technology

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 1034 cm−2 s−1 (1027 cm−2 s−1). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4π solid angle. Forward sampling calorimeters extend the pseudorapidity coverage to high values (|η| ≤ 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.

A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4ℓ decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is m_{H}=125.09±0.21 (stat)±0.11 (syst) GeV.

Wastewater treatment plants (WWTPs) are acting as routes of microplastics (MPs) to the environment, hence the urgent need to examine MPs in wastewaters and different types of sludge through sampling campaigns covering extended periods of time. In this study, the efficiency of a municipal WWTP to remove MPs from wastewater was studied by collecting wastewater and sludge samples once in every two weeks during a 3-month sampling campaign. The WWTP was operated based on the conventional activated sludge (CAS) process and a pilot-scale membrane bioreactor (MBR). The microplastic particles and fibers from both water and sludge samples were identified by using an optical microscope, Fourier Transform Infrared (FTIR) microscope and Raman microscope. Overall, the retention capacity of microplastics in the studied WWTP was found to be 98.3%. Most of the MP fraction was removed before the activated sludge process. The efficiency of an advanced membrane bioreactor (MBR) technology was also examined. The main related finding is that MBR permeate contained 0.4 MP/L in comparison with the final effluent of the CAS process (1.0 MP/L). According to this study, both microplastic fibers and particles are discharged from the WWTP to the aquatic environment.

Abstract Systematic literature reviews are an increasingly used review methodology to synthesize the existing body of literature in a field. However, editors complain about a high number of desk rejections because of a lack in quality. Poorly developed review articles are not published because of a perceived lack of contribution to the field. Our article supports authors of standalone papers and graduate students in the Entrepreneurship domain to write contribution-focused systematic reviews e.g. by providing a concrete guideline. Our article analyzes the strengths and weaknesses of a systematic literature review and how they can be overcome. Furthermore, we provide a combined list of highly ranked journals in the Entrepreneurship domain as a basis for quality appraisal. Finally, this article builds a scenario for the future of the systematic literature review methodology and shows how technological improvements have changed this methodology and what can be achieved in the future.

CO2 direct air capture (DAC) has been increasingly discussed as a climate change mitigation option. Despite technical advances in the past decade, there are still misconceptions about DAC's current and long-term costs as well as energy, water and area demands. This could undermine DAC's anticipated role in a neutral or negative greenhouse gas emission energy system, and influence policy makers. In this study, a literature review and techno-economic analyses of state-of-the-art DAC technologies are performed, wherein, DAC technologies are categorised as high temperature aqueous solutions (HT DAC) and low temperature solid sorbent (LT DAC) systems, from an energy system perspective. DAC capital expenditures, energy demands and costs have been estimated under two scenarios for DAC capacities and financial learning rates in the period 2020 to 2050. DAC system costs could be lowered significantly with commercialisation in the 2020s followed by massive implementation in the 2040s and 2050s, making them cost competitive with point source carbon capture and an affordable climate change mitigation solution. It is concluded that LT DAC systems are favourable due to lower heat supply costs and the possibility of using waste heat from other systems. CO2 capture costs of LT DAC systems powered by hybrid PV-Wind-battery systems for Moroccan conditions and based on a conservative scenario, without/with utilisation of free waste heat are calculated at 222/133, 105/60, 69/40 and 54/32 €/tCO2 in 2020, 2030, 2040 and 2050, respectively. These new findings could enhance DAC's role in a successful climate change mitigation strategy.

Combined ATLAS and CMS measurements of the Higgs boson production and decay rates, as well as constraints on its couplings to vector bosons and fermions, are presented. The combination is based on the analysis of five production processes, namely gluon fusion, vector boson fusion, and associated production with a W or a Z boson or a pair of top quarks, and of the six decay modes H → ZZ, W W , γγ, ττ, bb, and μμ. All results are reported assuming a value of 125.09 GeV for the Higgs boson mass, the result of the combined measurement by the ATLAS and CMS experiments. The analysis uses the CERN LHC proton-proton collision data recorded by the ATLAS and CMS experiments in 2011 and 2012, corresponding to integrated luminosities per experiment of approximately 5 fb$^{−1}$ at $\sqrt{s}$=7 TeV and 20 fb−1 at $\sqrt{s}$=8 TeV. The Higgs boson production and decay rates measured by the two experiments are combined within the context of three generic parameterisations: two based on cross sections and branching fractions, and one on ratios of coupling modifiers. Several interpretations of the measurements with more model-dependent parameterisations are also given. The combined signal yield relative to the Standard Model prediction is measured to be 1.09 ± 0.11. The combined measurements lead to observed significances for the vector boson fusion production process and for the H → ττ decay of 5.4 and 5.5 standard deviations, respectively. The data are consistent with the Standard Model predictions for all parameterisations considered.

Purpose The purpose of this paper is to test the extension of the theory of planned behaviour (TPB) in an organic food buying context. Design/methodology/approach The relationships between subjective norms and attitudes and intention to buy organic food were studied by applying structural equation modelling. Findings The proposed modified model of the TPB model fitted the data better than the original model, implying that in the organic food‐buying context the role of subjective norms differs from the original theory of planned behaviour. In buying organic food subjective norms affected buying intention indirectly through attitude formation. In addition, results showed that the modified TPB model predicts intention to buy organic food better than the original model. Based on the results, it can be said that consumers' intentions to buy organic food can be predicted with their attitudes ( R 2 =0.558), which can further be predicted by subjective norms ( R 2 =0.374), and that behavioural intentions reliably predict self‐reported behaviour ( R 2 =0.824). Research limitations/implications First, this study concerned only organic bread and flour products, and therefore the results cannot be expected to explain consumer behaviour for all organically produced products. Second, just one retail channel of organic foods, a hypermarket, was examined. Since the different store formats have also very different characteristics (e.g. price level and number of products), it is likely that also the consumers' buying behaviour differs between different stores. Originality/value In past studies on organic food‐buying behaviour, the role of subjective norms has often been neglected – either they are not included in the models or their explanatory power has been weak.

This letter proposes a modification to the least-significant-bit (LSB) matching, a steganographic method for embedding message bits into a still image. In the LSB matching, the choice of whether to add or subtract one from the cover image pixel is random. The new method uses the choice to set a binary function of two cover pixels to the desired value. The embedding is performed using a pair of pixels as a unit, where the LSB of the first pixel carries one bit of information, and a function of the two pixel values carries another bit of information. Therefore, the modified method allows embedding the same payload as LSB matching but with fewer changes to the cover image. The experimental results of the proposed method show better performance than traditional LSB matching in terms of distortion and resistance against existing steganalysis.

The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

Abstract Review articles or literature reviews are a critical part of scientific research. While numerous guides on literature reviews exist, these are often limited to the philosophy of review procedures, protocols, and nomenclatures, triggering non-parsimonious reporting and confusion due to overlapping similarities. To address the aforementioned limitations, we adopt a pragmatic approach to demystify and shape the academic practice of conducting literature reviews. We concentrate on the types, focuses, considerations, methods, and contributions of literature reviews as independent, standalone studies. As such, our article serves as an overview that scholars can rely upon to navigate the fundamental elements of literature reviews as standalone and independent studies, without getting entangled in the complexities of review procedures, protocols, and nomenclatures.

Climate change threats and the necessity to achieve global Sustainable Development Goals demand unprecedented economic and social shifts around the world, including a fundamental transformation of the global energy system. An energy transition is underway in most regions, predominantly in the power sector. This research highlights the technical feasibility and economic viability of 100% renewable energy systems including the power, heat, transport and desalination sectors. It presents a technology-rich, multi-sectoral, multi-regional and cost-optimal global energy transition pathway for 145 regional energy systems sectionalised into nine major regions of the world. This 1.5 °C target compatible scenario with rapid direct and indirect electrification via Power-to-X processes and massive defossilisation indicates substantial benefits: 50% energy savings, universal access to fresh water and low-cost energy supply. It also provides an energy transition pathway that could lead from the current fossil-based system to an affordable, efficient, sustainable and secure energy future for the world.

Results on two-particle angular correlations for charged particles emitted in proton-proton collisions at center-of-mass energies of 0.9, 2.36, and 7 TeV are presented, using data collected with the CMS detector over a broad range of pseudorapidity () and azimuthal angle (). Short-range correlations in , which are studied in minimum bias events, are characterized using a simple "independent cluster" parametrization in order to quantify their strength (cluster size) and their extent in (cluster decay width). Long-range azimuthal correlations are studied differentially as a function of charged particle multiplicity and particle transverse momentum using a 980 nb -1 data set at 7 TeV. In high multiplicity events, a pronounced structure emerges in the two-dimensional correlation function for particle pairs with intermediate p T of 1-3 GeV/c, 2.0 < || < 4.8 and 0. This is the first observation of such a long-range, near-side feature in two-particle correlation functions in pp or pp collisions.

Digital supply chain integration is becoming \ increasingly dynamic. Access to customer demand \ needs to be shared effectively, and product and service \ deliveries must be tracked to provide visibility in the \ supply chain. Business process integration is based on \ standards and reference architectures, which should \ offer end-to-end integration of product data. \ Companies operating in supply chains establish \ process and data integration through the specialized \ intermediate companies, whose role is to establish \ interoperability by mapping and integrating companyspecific \ data for various organizations and systems. \ This has typically caused high integration costs, and \ diffusion is slow. This paper investigates the \ requirements and functionalities of supply chain \ integration. Cloud integration can be expected to offer \ a cost-effective business model for interoperable \ digital supply chains. We explain how supply chain \ integration through the blockchain technology can \ achieve disruptive transformation in digital supply \ chains and networks.

A single neuron with Hebbian-type learning for the connection weights, and with nonlinear internal feedback, has been shown to extract the statistical principal components of its stationary input pattern sequence. A generalization of this model to a layer of neuron units is given, called the Subspace Network, which yields a multi-dimensional, principal component subspace. This can be used as an associative memory for the input vectors or as a module in nonsupervised learning of data clusters in the input space. It is also able to realize a powerful pattern classifier based on projections on class subspaces. Some classification results for natural textures are given.

During the COVID-19 pandemic, unusual consumer behavior, such as hoarding toilet paper, was reported globally. We investigated this behavior when fears of consumer market disruptions started circulating, to capture human behavior in this unique situation. Based on the stimulus-organism-response (S-O-R) framework, we propose a structural model connecting exposure to online information sources (environmental stimuli) to two behavioral responses: unusual purchases and voluntary self-isolation. To test the proposed model, we collected data from 211 Finnish respondents via an online survey, and carried out analysis using PLS-SEM. We found a strong link between self-intention to self-isolate and intention to make unusual purchases, providing empirical evidence that the reported consumer behavior was directly linked to anticipated time spent in self-isolation. The results further revealed exposure to online information sources led to increased information overload and cyberchondria. Information overload was also a strong predictor of cyberchondria. Perceived severity of the situation and cyberchondria had significant impacts on people's intention to make unusual purchases and voluntarily self-isolate. Future research is needed to confirm the long-term effects of the pandemic on consumer and retail services.

This paper describes the CMS trigger system and its performance during Run 1 of the LHC. The trigger system consists of two levels designed to select events of potential physics interest from a GHz (MHz) interaction rate of proton-proton (heavy ion) collisions. The first level of the trigger is implemented in hardware, and selects events containing detector signals consistent with an electron, photon, muon, $\tau$ lepton, jet, or missing transverse energy. A programmable menu of up to 128 object-based algorithms is used to select events for subsequent processing. The trigger thresholds are adjusted to the LHC instantaneous luminosity during data taking in order to restrict the output rate to 100 kHz, the upper limit imposed by the CMS readout electronics. The second level, implemented in software, further refines the purity of the output stream, selecting an average rate of 400 Hz for offline event storage. The objectives, strategy and performance of the trigger system during the LHC Run 1 are described.

Purpose Within a very short period of time, the worldwide pandemic triggered by the novel coronavirus has not only claimed numerous lives but also caused severe limitations to daily private as well as business life. Just about every company has been affected in one way or another. This first empirical study on the effects of the COVID-19 crisis on family firms allows initial conclusions to be drawn about family firm crisis management. Design/methodology/approach Exploratory qualitative research design based on 27 semi-structured interviews with key informants of family firms of all sizes in five Western European countries that are in different stages of the crisis. Findings The COVID-19 crisis represents a new type and quality of challenge for companies. These companies are applying measures that can be assigned to three different strategies to adapt to the crisis in the short term and emerge from it stronger in the long run. Our findings show how companies in all industries and of all sizes adapt their business models to changing environmental conditions within a short period of time. Finally, the findings also show that the crisis is bringing about a significant yet unintended cultural change. On the one hand, a stronger solidarity and cohesion within the company was observed, while on the other hand, the crisis has led to a tentative digitalization. Originality/value To the knowledge of the authors, this is the first empirical study in the management realm on the impacts of COVID-19 on (family) firms. It provides cross-national evidence of family firms' current reactions to the crisis.

Sustainable business model innovation (SBMI) in large multinational corporations is increasingly perceived as a key driver for competitive advantage and corporate sustainability. While the SBMI literature acknowledges that corporations require dynamic capabilities to innovate their business model for sustainability, the role of organization design to nurture dynamic capabilities for this purpose has been scantly addressed. By taking a qualitative research approach, we address how organization design affects dynamic capabilities needed for SBMI. Accordingly, from an organization design perspective, we identified barriers and drivers on three levels: the institutional, the strategic, and the operational. The contributions of our study are threefold. First, we contribute to a recent discussion on how organizational design affects dynamic capabilities needed for business model innovation. Second, we present a multi-level framework to show how interconnected barriers and drivers obstruct or enable SBMI. Third, our study answers a call to advance theoretical perspectives on SBMI.

Properties of the Higgs boson with mass near 125[Formula: see text] are measured in proton-proton collisions with the CMS experiment at the LHC. Comprehensive sets of production and decay measurements are combined. The decay channels include [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] pairs. The data samples were collected in 2011 and 2012 and correspond to integrated luminosities of up to 5.1[Formula: see text] at 7[Formula: see text] and up to 19.7[Formula: see text] at 8[Formula: see text]. From the high-resolution [Formula: see text] and [Formula: see text] channels, the mass of the Higgs boson is determined to be [Formula: see text]. For this mass value, the event yields obtained in the different analyses tagging specific decay channels and production mechanisms are consistent with those expected for the standard model Higgs boson. The combined best-fit signal relative to the standard model expectation is [Formula: see text] at the measured mass. The couplings of the Higgs boson are probed for deviations in magnitude from the standard model predictions in multiple ways, including searches for invisible and undetected decays. No significant deviations are found.